Abstract:

The present invention invention provides methods, apparatus, and systems
for determining greenhouse gas (including carbon dioxide) emissions and
energy usage, costs and savings of individuals, families, homes,
buildings, businesses, or the like. User inputs specific to an end user
are accepted, and one or more of the user inputs are correlated with at
least one of historic data and modeled characteristics pertaining to
greenhouse gas emissions and energy usage to obtain at least one of
greenhouse gas emissions and energy usage corresponding to said one or
more of said user inputs. An overall greenhouse gas emissions and energy
usage can then be determined for the end user based on the greenhouse
emissions and energy usage corresponding to the one or more of the user
inputs. A specific impact of a particular user action on the end user's
overall greenhouse gas emissions and energy usage may also be calculated.

Claims:

1. A computerized method for determining greenhouse gas emissions and
energy usage, comprising:accepting user inputs specific to an end
user;correlating one or more of said user inputs with at least one of
historic data and modeled characteristics pertaining to greenhouse gas
emissions and energy usage to obtain at least one of greenhouse gas
emissions and energy usage corresponding to said one or more of said user
inputs; anddetermining an overall greenhouse gas emissions and energy
usage for said end user based on said greenhouse emissions and energy
usage corresponding to said one or more of said user inputs.

2. A computerized method in accordance with claim 1, wherein said user
inputs comprise details regarding at least one of home, work, travel, and
consumption of goods.

3. A computerized method in accordance with claim 1, wherein:said overall
greenhouse gas emissions and energy usage comprise direct and indirect
greenhouse gas emissions and energy usage;said direct greenhouse gas
emissions and energy usage account for a direct impact of at least one of
actions taken by the end user and performance of products purchased by
the end user; andsaid indirect greenhouse gas emissions and energy usage
corresponds to one or more of material sourcing, manufacture,
distribution, retail, consumption and post-consumption of products
purchased by the end user.

4. A computerized method in accordance with claim 1, further
comprising:providing home, work, shopping and travel categories of
greenhouse gas emissions and energy usage;enabling a selection of one or
more of said categories; anddetermining a portion of said overall
greenhouse gas emissions and energy usage corresponding to said one or
more selected categories;wherein:said portion of said overall greenhouse
gas emissions and energy usage for said home category is based on at
least one of water heating, space heating, space cooling and appliance
information for said end user's home;said portion of said overall
greenhouse gas emissions and energy usage for said work category is based
on at least one of electricity and natural gas information for said end
user's work environment;said portion of said overall greenhouse gas
emissions and energy usage for said shopping category is based on at
least one of food, alcohol, hotel, housing, healthcare, and miscellaneous
expenditures and consumption information; andsaid portion of said overall
greenhouse gas emissions and energy usage for said travel category is
based on at least one of vehicle, airplane, and miscellaneous
transportation expenditures and information.

5. A computerized method in accordance with claim 4, wherein said user
inputs for said home category comprise at least one of zip code, heating
equipment type, cooling equipment type, heating fuel, water heater type,
water heater size, water heater fuel, space heating equipment, space
cooling equipment, age of heating and cooling equipment, residence type,
residence construction material information, year of residence
construction, square footage, number of rooms, number of heating degree
days per year, number of cooling degree days per year, yearly household
income, lighting type and usage information, home office equipment
information, major appliance information, small appliance information,
day and night thermostat settings, census division based on zip code,
typical temperature setting for wash cycle of washing machine, stove
fuel, number of people in residence, average monthly fuel usage, average
monthly fuel cost, swimming pool information, spa information, number of
televisions, number of computers, relative urbanity of area of home,
aquarium information, separate freezer, water bed ownership
characteristics.

6. A computerized method in accordance with claim 5, wherein:said zip code
input is linked to a corresponding weather location; andenergy usage
corresponding to a default residence type for said corresponding weather
location is determined based on historical weather patterns for said
weather location;said overall greenhouse gas emissions and energy usage
is determined from the energy usage corresponding to the default
residence type.

7. A computerized method in accordance with claim 6, further comprising
mapping the zip code input to a regression analysis of at least one of
current Department of Energy Residential Energy Consumption Survey data,
National Climate Data Center Climate Division data, U.S. Census Data,
American Housing Survey Data, public energy consumption data, and private
energy consumption data.

8. A computerized method in accordance with claim 6, further
comprising:automatically obtaining specific residence information from
computerized public records; andrefining said default residence type
based on said specific residence information;wherein said specific
residence information includes at least one of residence type, square
footage, year built, heating equipment type, cooling equipment type, fuel
type, insulation type, number of rooms, and number of individuals in
residence.

9. A computerized method in accordance with claim 6, wherein said overall
greenhouse gas emissions and energy usage corresponding to said default
residence type is modified based on other of said user inputs.

10. A computerized method in accordance with claim 5, wherein said overall
greenhouse gas emissions and energy usage is subdivided into a plurality
of home end-uses and an overall home footprint.

11. A computerized method in accordance with claim 4, wherein said user
inputs for said home category include home fuel payment information.

12. A computerized method in accordance with claim 11, wherein said fuel
payment information comprises fuel cost information, said method further
comprising:correlating said fuel cost information with a utility provider
based on a database of utility providers for the end user's zip
code;obtaining up-to-date pricing information for said utility
provider;determining fuel usage based on said pricing information.

14. A computerized method in accordance with claim 11, wherein:said fuel
payment information is linked to a database containing annual fuel use
curves for a corresponding fuel type used in the residence; andsaid
annual fuel use curve is determined from historical weather and
temperature characteristics in a weather location corresponding to the
zip code.

15. A computerized method in accordance with claim 5, further
comprising:determining fuel usage by a simulation of fuel usage based on
the zip code and at least one of the residence type, the heating
equipment type, the cooling equipment type, the water heater type, the
space heating equipment, the space cooling equipment, the major
appliances, and the small appliances.

16. A computerized method in accordance with claim 15, wherein:default
inputs are provided for at least one of the residence type, the heating
equipment type, the cooling equipment type, the water heater type, the
space heating equipment, the space cooling equipment, the major
appliances, and the small appliances; andsaid default inputs are based on
common types of equipment in the weather location.

17. A computerized method in accordance with claim 4, wherein said user
inputs for said travel category comprise at least one of vehicle
information, flight history information, vehicle rental information, taxi
usage history, and public transportation usage habits.

18. A computerized method in accordance with claim 17, wherein:yearly fuel
consumption for each vehicle identified in said vehicle information is
determined based on one of historical mileage data or user input actual
mileage data for each of said identified vehicle; andsaid yearly fuel
consumption is converted to yearly greenhouse gas emissions for each
vehicle using conversion factors for converting fuel type to carbon
dioxide.

19. A method in accordance with claim 17, wherein:said flight history
information comprises one of: (a) specific flight information for each
flight taken, including at least one of flight length, flight origin and
destination, plane type, plane age, and layover information; and (b)
estimate of number of flights taken and length of flights taken;a flight
class is determined for each flight based on the flight length;carbon
dioxide emissions are determined for each flight based on an emissions
factor for the flight class and the flight length.

21. A computerized method in accordance with claim 20, wherein said user
input further comprises one of home office, manufacturing,
non-manufacturing, and educational.

22. A computerized method in accordance with claim 21, wherein:in the
event of an entry of said non-manufacturing user input, a building type
user input may be selected from one of: school; supermarket or grocery
store; restaurant; hospital; doctor or dentist office; hotel or motel;
retail store; professional or administrative office; social space; police
or fire department; place of religious worship; post office or copy
center; dry cleaners, laundromat or beauty parlor; auto service or gas
station; and warehouse or storage facility; andper worker electricity and
fuel usage corresponding to a selected building type is determined, at
least in part, from historical energy consumption survey data.

24. A computerized method in accordance with claim 23, wherein:industry
specific user inputs corresponding to said manufacturing user inputs are
made available;the at least one of the total fuel consumption, the per
worker fuel consumption, the total electricity consumption, and the total
natural gas consumption corresponding to the selected manufacturing
sector is refined based on said industry specific user inputs.

25. A computerized method in accordance with claim 21, wherein:in the
event of an entry of said educational user input, an educational capacity
user input may be selected from one of a teacher input or a student input
and a facility type may be selected from one of kindergarten, elementary
school, middle school, high school, or college.

26. A computerized method in accordance with claim 25, wherein:in
determining overall greenhouse gas emissions and fuel usage corresponding
to said educational user input, different multiplication factors are
assigned based on whether the teacher user input or the student user
input are selected;a first multiplication factor for said teacher user
input and said college user input is based on a per worker value;a second
multiplication factor for said kindergarten user input, said elementary
school user input, said middle school user input, and said high school
user input is based on a per worker and student value, such that the
overall greenhouse gas emissions and fuel usage per kindergarten,
elementary school, middle school or high school student for a selected
facility type will be less than the overall greenhouse gas emissions and
fuel usage per teacher or college student in said selected facility type.

27. A computerized method in accordance with claim 25, wherein said
educational user inputs are correlated with historical data for similar
educational buildings in a corresponding census division or zip code.

30. A computerized method in accordance with claim 29, further
comprising:correlating said user inputs with historical survey data and
reference categories for determination of corresponding multiplication
factors;multiplying dollars spent for each of said user inputs with a
corresponding multiplication factor to determine corresponding greenhouse
gas emissions and energy usage for each of said user inputs.

32. A computerized method in accordance with claim 1, wherein said
historic data comprises at least one of government data, private data,
public energy study data, and data contained in databases administered by
universities and government agencies.

33. A computerized method in accordance with claim 32, wherein said
government data comprises data from at least one of U.S. Department of
Energy, U.S. Environmental Protection Agency, U.S. Department of Labor,
U.S. Department of Commerce, U.S. Department of Transportation, U.S.
Census Bureau, and data from databases maintained by other government
agencies.

34. A computerized method in accordance with claim 1, further
comprising:prompting said end user for additional user inputs based on
selected user inputs to further refine the overall greenhouse gas
emissions and energy usage.

35. A computerized method in accordance with claim 1 further
comprising:calculating a specific impact of a particular user action on
the end user's overall greenhouse gas emissions and energy usage;wherein
said impact is presented in the form of at least one of energy savings or
increase, greenhouse gas reduction or increase, cost savings or increase,
and resource savings or increase for the particular user action.

36. A computerized method in accordance with claim 35, further
comprising:providing comparisons of said impact between alternate choices
for a particular user action.

37. A computerized method in accordance with claim 35, wherein said
overall greenhouse gas emissions and energy usage for said end user is
updated automatically upon entry of said particular user action.

38. A computerized method in accordance with claim 35, further
comprising:providing at least one of an Internet application or a
downloadable application for at least one of: (a) said determining of
said overall greenhouse gas emissions and energy usage for said end user;
and (b) said calculating of said specific impact of a particular user
action or purchase; andproviding a customizable user interface for at
least one of said Internet application and said downloadable application.

39. A computerized method in accordance with claim 38, further comprising
providing a link to at least one of selected individuals or selected
companies for comparison of overall greenhouse gas emissions and energy
usage.

40. A computerized method in accordance with claim 39, further comprising
providing at least one of: updates on said selected individuals or
companies greenhouse gas emissions and energy usage status; real-time
chats with said selected individuals or individuals at said selected
companies; energy saving product and service updates; energy and cost
savings planning information; fuel cost updates from various regional
suppliers, informational material regarding energy savings and reduction
of greenhouse gas emissions; community event information; online shopping
for recommended products and services; displays relating to said overall
greenhouse gas emissions and energy usage and subcategories of said
overall greenhouse gas emissions and energy usage; access to custom
product and action recommendations tailored to said end user based on
said user inputs; energy saving actions recommended based on actions
taken by users with similar demographic characteristics; and energy
savings actions prioritized based on payback period and discount rate.

41. A computerized method in accordance with claim 1, further
comprising:providing a virtual world environment for said end user based
on said user inputs; andcalculating a specific impact of a particular
user action taken in the virtual world environment on the end user's
overall greenhouse gas emissions and energy usage.

42. A computerized method in accordance with claim 41, further comprising
at least one of:providing guidance and recommendations to said end user
for reducing said overall greenhouse gas emissions and energy usage in
said virtual world environment;enabling virtual contests between
individuals in said virtual world for reduction of said overall
greenhouse gas emissions and energy usage in said virtual world
environment; andenabling a multi-user virtual game where points are
awarded based on reduction of said overall greenhouse gas emissions and
energy usage in said virtual world environment.

43. A system for determining greenhouse gas emissions and energy usage,
comprising:a user interface adapted to accept user inputs specific to an
end user;a communications link to at least one database;processing means
adapted to accept said user inputs from said user interface and to access
said at least one database via said communications link in order to
correlate one or more of said user inputs with at least one of historic
data and modeled characteristics pertaining to greenhouse gas emissions
and energy usage contained in said at least one database to obtain at
least one of greenhouse gas emissions and energy usage corresponding to
said one or more of said user inputs; andwherein said processing means
determines an overall greenhouse gas emissions and energy usage for said
end user based on said greenhouse emissions and energy usage
corresponding to said one or more of said user inputs.

Description:

[0001]This application claims the benefit of U.S. Provisional Patent
Application No. 61/188,817, filed Aug. 12, 2008, which is incorporated
herein and made a part hereof by reference.

BACKGROUND OF THE INVENTION

[0002]The present invention relates to the field of greenhouse gas
emissions and energy usage. More specifically, the present invention
provides methods, apparatus, and systems for determining the greenhouse
gas emissions and energy usage, as well as associated dollar costs and
savings of individuals, families, homes, buildings, businesses, or the
like. The present invention also provides methods, apparatus, and systems
for determining the impact of particular actions on overall greenhouse
gas emissions and/or energy usage.

[0003]With increasing energy costs and growing concern about global
warming, individuals and companies have become increasingly concerned
with their impact on the environment and in particular their contribution
to climate change. An individual or organization's impact on or
contribution to climate change has come to be known as a "carbon
footprint". The term "carbon footprint" as used herein should be
understood to include greenhouse gases in addition to carbon dioxide.

[0004]There are several prior art carbon footprint calculators, such as
Yahoo! Green or An Inconvenient Truth Calculator, which yield outputs
that apply across individuals in a particular zip code, state or even
nation. However, these prior art calculators are unable to provide a
carbon footprint determination that is uniquely tailored to a specific
individual or business. Further, none of the available prior art
calculators is capable of determining changes in the carbon footprint
based on new or proposed actions taken or contemplated by an individual
or a business at a high resolution and personalized degree of
specificity.

[0005]It would be advantageous to provide accurate estimates of carbon
dioxide emissions and energy usage that apply specifically to an
individual, family, business, home or building. It would also be
advantageous to determine the impact that specific actions or proposed
actions would have on the determined estimates, so that the relative
impact of the action on global warming can be determined.

[0006]The methods, apparatus, and systems of the present invention provide
the foregoing and other advantages.

SUMMARY OF THE INVENTION

[0007]The present invention relates to methods, apparatus, and systems for
determining greenhouse gas (including carbon dioxide) emissions and
energy usage, costs and savings of individuals, families, homes,
buildings, businesses, or the like. Although the present invention is
described below in connection with the determination of an individual's
carbon footprint, those skilled in the art will appreciate that the
present invention can be applied to families, homes, buildings,
businesses, or the like and may be include a wide variety of resources,
energy systems and greenhouse gases.

[0008]The present invention, developed by Efficiency 2.0, LLC of New York
(formerly Climate Culture, LLC), includes four major components:

[0009]1. Energy Mapping Software (EMS)--determines an individual's energy
use and greenhouse gas footprint based on a variety of forms of data and
algorithms. The EMS provides a comprehensive, personalized and granular
estimate of an individual's energy use, greenhouse gas (including carbon
dioxide) emissions, and other greenhouse gas emissions (including
methane, nitrous oxide, and various halocarbons) across areas including
(but not limited to) home, work, travel, recreation, dining, and shopping
habits, including resource usage, direct and indirect energy usage and
greenhouse gas emissions.

[0010]2. Personal Energy Advisor--determines the change (or potential
change) in energy use and greenhouse gas emissions, as well as the dollar
cost, dollar savings, and other resource savings based on a change in an
individual's actions and purchases (or potential actions and purchases)
from the entire scope of behavioral and purchasing decisions individuals
and businesses confront in their ordinary lives and business operations,
respectively.

[0011]3. Community Connect--combines the Energy Mapping Software and
Personal Energy Advisor to create a personalized and automated online
assistant capable of helping an individual or business understand its
specific impact on global warming, energy supply, and other resources
through lifestyle habits, actions taken and purchases made. Community
Connect also integrates the energy advisory service with online community
features that enable individuals to compare and compete with others in a
host of sophisticated ways.

[0012]4. Climate Culture Virtual World Game and Social Network (CCVW)--is
a virtual networked environment that mirrors the actual global warming
impact of the individual and those in the individual's social network
community. The Climate Culture Virtual World Game creates a new process
for enabling a consumer or organization to understand and decrease its
global warming impact. The Climate Culture Virtual World Game
accomplishes this goal by enabling users to engage one another in a
competitive and collaborate virtual space. The Climate Culture Virtual
World Game is a game aimed at consumers and businesses which enables them
to reduce their global warming impact by providing reliable estimates of
carbon dioxide and energy usage, as well as associated reductions in
usage.

[0013]In accordance with one example embodiment of the present invention,
a computerized method for determining greenhouse gas emissions and energy
usage is provided. User inputs specific to an end user are accepted, and
one or more of the user inputs are correlated with at least one of
historic data and modeled characteristics pertaining to greenhouse gas
emissions and energy usage to obtain at least one of greenhouse gas
emissions and energy usage corresponding to the one or more of the user
inputs. Overall greenhouse gas emissions and energy usage can then be
determined for the end user based on the greenhouse emissions and energy
usage corresponding to the one or more of the user inputs.

[0014]Note it should be appreciated that the term "end user" is used
herein to include any individual, group of individuals, entity, business,
non-profit company, university, and the like, including any other "user"
that may have a carbon footprint.

[0015]The user inputs may comprise details regarding at least one of home,
work, travel, and consumption of goods.

[0016]In one example embodiment, the overall greenhouse gas emissions and
energy usage may comprise direct and indirect greenhouse gas emissions
and energy usage. The direct greenhouse gas emissions and energy usage
account for a direct impact of at least one of actions taken by the end
user and performance of products purchased by the end user. The indirect
greenhouse gas emissions and energy usage corresponds to one or more of
material sourcing, manufacture, distribution, retail, consumption and
post-consumption of products purchased by the end user.

[0017]Home, work, shopping and travel categories of greenhouse gas
emissions and energy usage may be provided. The end user may be enabled
to make a selection of one or more of the categories, such that a portion
of the overall greenhouse gas emissions and energy usage corresponding to
the one or more selected categories can be determined. The portion of the
overall greenhouse gas emissions and energy usage for the home category
may be based on at least one of water heating, space heating, space
cooling, appliance information, and the like for the end user's home. The
portion of the overall greenhouse gas emissions and energy usage for the
work category may be based on at least one of electricity and natural gas
information (and optionally additional information as discussed below)
for the end user's work environment. The portion of the overall
greenhouse gas emissions and energy usage for the shopping category may
be based on at least one of food, alcohol, hotel, housing, healthcare,
and miscellaneous expenditures and consumption information, and the like.
The portion of the overall greenhouse gas emissions and energy usage for
the travel category may be based on at least one of vehicle, airplane,
and miscellaneous transportation expenditures and information, and the
like.

[0018]The user inputs for the home category may comprise at least one of
zip code, heating equipment type, cooling equipment type, heating fuel,
water heater type, water heater size, water heater fuel, space heating
equipment, space cooling equipment, age of heating and cooling equipment,
residence type, residence construction material information, year of
residence construction, square footage, number of rooms, number of
heating degree days per year, number of cooling degree days per year,
yearly household income, lighting type and usage information, home office
equipment information, major appliance information, small appliance
information, day and night thermostat settings, census division based on
zip code, typical temperature setting for wash cycle of washing machine,
stove fuel, number of people in residence, average monthly fuel usage,
average monthly fuel cost, swimming pool information, spa information,
number of televisions, number of computers, relative urbanity of area of
home, aquarium information, separate freezer, water bed ownership
characteristics, and the like.

[0019]In one example embodiment, the zip code input may be linked to a
corresponding weather location. Energy usage corresponding to a default
residence type for the corresponding weather location may be determined
based on historical weather patterns for that weather location. The
overall greenhouse gas emissions and energy usage may then be determined
from the energy usage corresponding to the default residence type.

[0020]The zip code input may be mapped to a regression analysis of at
least one of current Department of Energy Residential Energy Consumption
Survey data, National Climate Data Center Climate Division data, U.S.
Census Data, American Housing Survey Data, public energy consumption
data, private energy consumption data, and the like.

[0021]In addition, specific residence information may be automatically
obtained from computerized public records. The default residence type may
be refined based on the specific residence information obtained in this
manner. The specific residence information may include at least one of
residence type, square footage, year built, heating equipment type,
cooling equipment type, fuel type, insulation type, number of rooms,
number of individuals in residence, and the like.

[0022]The overall greenhouse gas emissions and energy usage corresponding
to the default residence type may be modified based on other of the user
inputs.

[0023]The overall greenhouse gas emissions and energy usage may be
subdivided into a plurality of home end-uses and an overall home
footprint.

[0024]The user inputs for the home category may include home fuel payment
information. The fuel payment information may comprise fuel cost
information. Where such fuel cost information is provided, this fuel cost
information may be correlated with a utility provider based on a database
of utility providers for the end user's zip code. Up-to-date pricing
information may then be obtained for the utility provider, and the fuel
usage can then be determined based on this pricing information.

[0026]In an alternate embodiment, the fuel payment information may be
linked to a database containing annual fuel use curves for a
corresponding fuel type used in the residence. The annual fuel use curve
may be determined from historical weather and temperature characteristics
in a weather location corresponding to the zip code.

[0027]In a further alternate embodiment, fuel usage may be determined by a
simulation of fuel usage based on the zip code and at least one of the
residence type, the heating equipment type, the cooling equipment type,
the water heater type, the space heating equipment, the space cooling
equipment, the major appliances, the small appliances, and the like.
Default inputs may be provided for at least one of the residence type,
the heating equipment type, the cooling equipment type, the water heater
type, the space heating equipment, the space cooling equipment, the major
appliances, the small appliances, and the like. These default inputs may
be based on common types of equipment in the weather location.

[0028]The user inputs for the travel category may comprise at least one of
vehicle information, flight history information, vehicle rental
information, taxi usage history, public transportation usage habits, and
the like. Yearly fuel consumption for each vehicle identified in the
vehicle information may be determined based on one of historical mileage
data or user input actual mileage data for each of the identified
vehicles. The yearly fuel consumption may then be converted to yearly
greenhouse gas emissions for each vehicle using conversion factors for
converting fuel type to carbon dioxide.

[0029]The flight history information may comprise one of: (a) specific
flight information for each flight taken, including at least one of
flight length, flight origin and destination, plane type, plane age,
layover information, and the like; and (b) estimate of number of flights
taken and length of flights taken. A flight class may be determined for
each flight based on the flight length. Carbon dioxide emissions may then
be determined for each flight based on an emissions factor for the flight
class and the flight length.

[0031]The user input may further comprise one of home office,
manufacturing, non-manufacturing, and educational. In the event of an
entry of the non-manufacturing user input, a building type user input may
be selected from one of: school; supermarket or grocery store;
restaurant; hospital; doctor or dentist office; hotel or motel; retail
store; professional or administrative office; social space; police or
fire department; place of religious worship; post office or copy center;
dry cleaners, laundromat or beauty parlor; auto service or gas station;
and warehouse or storage facility. Per worker electricity and fuel usage
corresponding to a selected building type may be determined, at least in
part, from historical energy consumption survey data.

[0032]In the event of an entry of the manufacturing user input, a
manufacturing sector user input may be selected from one of: food;
beverage and tobacco products; textile mills; textile product mills;
apparel; leather products; wood products; paper; printing-related
support; petroleum and coal products; chemicals; plastics and rubber
products; nonmetallic mineral products; primary metals; fabricated metal
products; machinery; computer and electronic products; electrical
equipment; transportation equipment; furniture and related products; and
miscellaneous products. At least one of total fuel consumption, per
worker fuel consumption, total electricity consumption, and total natural
gas consumption corresponding to a selected manufacturing sector may be
determined, at least in part, based on a historical census data for the
selected manufacturing sector and geographic location data.

[0033]In addition, industry specific user inputs corresponding to the
manufacturing user inputs may be made available. The at least one of the
total fuel consumption, the per worker fuel consumption, the total
electricity consumption, and the total natural gas consumption
corresponding to the selected manufacturing sector is refined based on
the industry specific user inputs.

[0034]In the event of an entry of the educational user input, an
educational capacity user input may be selected from one of a teacher
input or a student input and a facility type may be selected from one of
kindergarten, elementary school, middle school, high school, or college.
In determining overall greenhouse gas emissions and fuel usage
corresponding to the educational user input, different multiplication
factors are assigned based on whether the teacher user input or the
student user input are selected. For example, a first multiplication
factor for the teacher user input and the college user input may be based
on a per worker value, while a second multiplication factor for the
kindergarten user input, the elementary school user input, the middle
school user input, and the high school user input may be based on a per
worker and student value, such that the overall greenhouse gas emissions
and fuel usage per kindergarten, elementary school, middle school or high
school student for a selected facility type will be less than the overall
greenhouse gas emissions and fuel usage per teacher or college student in
the selected facility type.

[0038]The historic data may comprises at least one of government data,
private data, public energy study data, data contained in databases
administered by universities and government agencies, and the like. For
example, the government data may comprise data from at least one of U.S.
Department of Energy, U.S. Environmental Protection Agency, U.S.
Department of Labor, U.S. Department of Commerce, U.S. Department of
Transportation, U.S. Census Bureau, and data from databases maintained by
other government agencies.

[0039]In a further example embodiment of the present invention, the end
user may be prompted for additional user inputs based on selected user
inputs to further refine the overall greenhouse gas emissions and energy
usage.

[0040]In another example embodiment, a specific impact of a particular
user action on the end user's overall greenhouse gas emissions and energy
usage may be calculated. The impact may be presented in the form of at
least one of energy savings or increase, greenhouse gas reduction or
increase, cost savings or increase, and resource savings or increase for
the particular user action. In addition, comparisons of the impact
between alternate choices for a particular user action may be provided.

[0041]The overall greenhouse gas emissions and energy usage for the end
user may be updated automatically upon entry of a particular user action.

[0042]At least one of an Internet application or a downloadable
application may be provided for at least one of: (a) the determining of
the overall greenhouse gas emissions and energy usage for the end user;
and (b) the calculating of the specific impact of a particular user
action or purchase.

[0043]A customizable user interface may be provided for at least one of
the Internet application and the downloadable application. At least one
of the Internet application and the downloadable application may be
adapted to run on a cellular phone, a personal digital assistant, a
laptop computer, a desktop computer, a netbook, or the like.

[0044]In a further example embodiment, a link to at least one of selected
individuals or selected companies may be provided for comparison of
overall greenhouse gas emissions and energy usage.

[0045]In addition, the present invention may provide at least one of:
updates on the selected individuals or companies greenhouse gas emissions
and energy usage status; real-time chats with the selected individuals or
individuals at the selected companies; energy saving product and service
updates; energy and cost savings planning information; fuel cost updates
from various regional suppliers, informational material regarding energy
savings and reduction of greenhouse gas emissions; community event
information; online shopping for recommended products and services;
displays relating to the overall greenhouse gas emissions and energy
usage and subcategories of the overall greenhouse gas emissions and
energy usage; access to custom product and action recommendations
tailored to the end user based on the user inputs; energy saving actions
recommended based on actions taken by users with similar demographic
characteristics; energy savings actions prioritized based on payback
period and discount rate, and similar features and functionality.

[0046]In another example embodiment, a virtual world environment may be
provided for the end user based on the user inputs. A calculation of a
specific impact of a particular user action taken in the virtual world
environment on the end user's overall greenhouse gas emissions and energy
usage may be made. Guidance and recommendations to the end user for
reducing the overall greenhouse gas emissions and energy usage in the
virtual world environment may be provided. Virtual contests between
individuals in the virtual world for reduction of the overall greenhouse
gas emissions and energy usage in the virtual world environment may be
enabled. In addition, a multi-user virtual game where points are awarded
based on reduction of the overall greenhouse gas emissions and energy
usage in the virtual world environment may also be enabled.

[0047]The present invention also includes apparatus and systems for
determining greenhouse gas emissions and energy usage. In one system
embodiment, a user interface adapted to accept user inputs specific to an
end user is provided. A communications link to at least one database is
also provided. Processing means adapted to accept the user inputs from
the user interface and to access the at least one database via the
communications link is also provided. The processing means is adapted to
correlate one or more of the user inputs with at least one of historic
data and modeled characteristics pertaining to greenhouse gas emissions
and energy usage contained in the at least one database to obtain at
least one of greenhouse gas emissions and energy usage corresponding to
the one or more of the user inputs. The processing means can then
determine an overall greenhouse gas emissions and energy usage for the
end user based on the greenhouse emissions and energy usage corresponding
to the one or more of the user inputs.

[0048]The system embodiments may also include the features and
functionality discussed above in connection with the methods of the
present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0049]The present invention will hereinafter be described in conjunction
with the appended drawing figures, wherein like reference numerals denote
like elements, and:

[0050]FIG. 1 shows a simplified block diagram of an example embodiment of
a system for implementing the present invention;

[0051]FIG. 2 shows a flow diagram of an example embodiment of the Energy
Mapping Software provided in accordance with the present invention; and

[0052]FIG. 3 shows a flow diagram of an example embodiment of the Personal
Energy Advisor Software provided in accordance with the present
invention.

DETAILED DESCRIPTION

[0053]The ensuing detailed description provides exemplary embodiments
only, and is not intended to limit the scope, applicability, or
configuration of the invention. Rather, the ensuing detailed description
of the exemplary embodiments will provide those skilled in the art with
an enabling description for implementing an embodiment of the invention.
It should be understood that various changes may be made in the function
and arrangement of elements without departing from the spirit and scope
of the invention as set forth in the appended claims.

[0054]The present invention provides methods, apparatus, and systems for
greenhouse gas footprint monitoring. More particularly, the present
invention provides a comprehensive, high-resolution, and helpful process
for quantifying and reducing global warming impact. Global warming impact
includes energy use, carbon dioxide emissions, emissions of other
greenhouse gases (including methane, nitrous oxide, and halocarbons), and
various physical resources. The methods, apparatus, and systems of the
present invention maximize the likelihood of an output corresponding with
the user's actual output under the widest range of user inputs.

[0055]The present invention is comprised of two hierarchically integrated
and normalized sets of algorithms. The Energy Mapping Software determines
a user's energy and other resource use as well as greenhouse gas
emissions based on a range of no more than 5 to more than 100 inputs. The
Personal Energy Advisor, which incorporates and builds upon the Energy
Mapping Software outputs determines a user's energy and other resource
use and greenhouse gas emissions based on hundreds of actions and
purchases with thousands of potential inputs. The Personal Energy Advisor
also combines the baseline usage estimates from the Energy Mapping
Software with the behavioral and purchase modeling estimates, which
interact in a complex feedback mechanism by which increased information
in one algorithm can evolve the output from the other algorithm through a
wide array of intermediate values.

[0056]The present invention also makes use of web-based technology to
promote real-time energy use and greenhouse gas emissions monitoring. In
accordance with an example embodiment of the present invention, the user
input may be provided via a user interface presented on a website. The
user may login to a private page on the web site and enter inputs in
response to various queries, described in detail below. The user may
create a user profile and save the input information and resultant
calculations, so that they can be easily modified and updated at a later
time.

[0057]Among other things, the algorithms provided by the present invention
manipulate databases maintained by various external sources. The external
sources relied on by the present invention include the highest quality,
most current government, industry and custom databases. The present
invention runs simultaneous algorithms for any given operation to produce
no less than 10 discrete outputs per operation from a wide range of
default and/or user inputs. The present invention may then recommend
actions based on the user's personal preferences, energy use habits,
lifestyle characteristics, and the like through a sophisticated
recommendation algorithm that takes into account the end user's
demographic, psychographic and energy end use profiles.

[0058]The present invention displays, translates and builds upon its
outputs through a wide variety of interfaces that maximize the likelihood
of a user closely relating to the quantity output. User interfaces
provided in accordance with the present invention may also be a part of
the Community Connect and Climate Culture Virtual World Game and Social
Network, which may include a competitive and collaborative interactive
social network, a virtual world, interactive maps and visualization
layers, complex unit conversions and time tracking.

[0059]FIG. 1 is a simplified block diagram of an example embodiment of a
system for implementing the present invention. A user workstation 10 may
be provided with a user interface 12 adapted to accept user inputs
specific to an end user. A communications link (e.g., connection via
network 16) may be provided to at least one database (e.g., databases A,
B, . . . N). Processing means 14 may be provided, which may be adapted to
accept the user inputs from the user interface 12 and to access the at
least one database A, B, . . . N via the communications link in order to
correlate one or more of the user inputs with historic data pertaining to
greenhouse gas emissions and energy usage contained in the at least one
database, in order to obtain at least one of greenhouse gas emissions and
energy usage corresponding to the one or more of the user inputs. The
processing means 14 may then determine an overall greenhouse gas
emissions and energy usage for the end user based on the greenhouse
emissions and energy usage corresponding to the one or more of the user
inputs. It should be appreciated that the block diagram of FIG. 1 is
simplified for ease of explanation, and that the system may comprise
various additional elements and sub-elements as required to carry out the
software processes discussed below. For example, the system may comprise
a large number of separate user workstations 10, and a large number of
databases A, B, . . . N, the network 16 may comprise the Internet, as
well as public and private networks, local area networks, wide area
networks, and the like. Multiple processing means 14 may be provided
which may or may not be in communication with each other. Further, the
processing means 14 may include multiple computer processors, Internet
servers, storage devices, integrated databases, user profile information
storage, credit card processing features, electronic store functionality,
and the like.

[0060]The individual components of the present invention are described in
more detail below.

[0061]I. Energy Mapping Software

[0062]The Energy Mapping Software is an advanced and intuitive personal
energy use and greenhouse gas footprint calculator. The software spans a
wide range of databases and algorithms that interact to provide a
comprehensive and accurate estimate of an individual's greenhouse gas
emissions and energy use. A flowchart illustrating an example embodiment
of the Energy Mapping Software is shown in FIG. 2, which is explained in
more detail below. The processes described in the FIG. 2 flowchart may be
implemented on the system shown in FIG. 1.

[0063]Referring to FIG. 2, the Energy Mapping software incorporates all
aspects of a user's lifestyle, and provides an estimate of overall
greenhouse gas emissions and energy usage 136, which includes but is not
limited to greenhouse gas emissions and energy usage from the end user's
home (Home Footprint 128), travel (Travel Footprint 130), work (Work
Footprint 132), and shopping habits (Shopping Footprint 134). The
estimates, for example the estimates in each of these four
categories--home, work, travel and shopping--span direct carbon dioxide
emissions, such as burning gasoline in your car, and indirect emissions,
like those associated with manufacturing the products that are bought or
with delivering fuel to households. Accordingly, the software
incorporates direct and indirect carbon dioxide emissions across the
entire range of a user's affect on the climate.

[0064]The software is not only a comprehensive carbon footprint calculator
but also very granular. For instance, the software is not only capable of
estimating a user's home energy and carbon dioxide footprint, but it can
also categorize that footprint into various components, for example,
space heating, space cooling, water heating, lighting, large appliances,
small appliances, and the like. The same level of granularity applies to
the other three usage categories as well. The granularity of the software
helps a user discern precisely where lifestyle choices most affect energy
use and the climate. With this knowledge a user can readily answer a host
of interesting questions, like "How does my air conditioner usage in the
summer compare to my year-round driving emissions?" Or: "How do the
indirect emissions associated with buying groceries compare to those
associated with my computer usage at work?" Being able to differentiate
the impact of a user's various activities provides the first step towards
an understanding of meaningful behavioral changes that may help protect
the climate.

[0065]Perhaps just as important as the comprehensiveness, accuracy and
granularity of the software is the fact that it is also completely
customizable to the time and legitimacy budgets of its users. For
example, user inputs 100 may include answers to as few as 5 or more than
100 questions to receive a high-resolution footprint estimate that
applies exclusively to that end user. The software only requires
questions that users can readily answer, and it formats the questions so
users can answer in the most convenient way possible. It also guides the
user through the process of answering additional questions that provide
more refined footprint estimates if the user so chooses, informing the
user as to which inputs will have the greatest impact on output accuracy.
As a result, the software meets the needs of both ordinary people who are
typically strapped for time and the most demanding energy and climate
specialists who will settle for nothing less than the most precise
estimates possible.

[0066]The different components of a user's energy end-use characteristics
will be described in more detail below. Those skilled in the art will
appreciate that the present invention can be implemented with more or
less than the residential, commercial, travel and consumption categories
mentioned herein. Similarly, those skilled in the art will appreciate
that the present invention can be implemented using different categories
or functions to the same effect.

Home Footprint 128

[0067]The home energy use estimation model operates in one of two ways: a
Top-Down Bill Disaggregation Model 108 in cases where there is access to
user utility bills (e.g., electricity bills 104 and natural gas bills
106), and a Bottom-Up Energy Mapping Model 110 cases where there is not,
where specific user inputs 100 and zip code defaults 102 are utilized. In
FIG. 2, the dashed lines reflect relationships that may or may not occur
based on specific end user characteristics or user inputs 100. In
addition, it should be appreciated that outputs for electricity 112 and
natural gas 114 from the Top-Down Bill Disaggregation Model 108 supersede
those of the Bottom-Up Energy Mapping Model 110 when available.

Bottom-Up Energy Mapping Model 110

[0068]To produce a viable personalized energy use calculation in the
absence of available utility bills or user inputs, the energy use mapping
software employs a Bottom-Up Model 110 to estimate the mode household
energy use for space heating 124, cooling 120, water heating 126, and
appliances 122 for every zip code in the country. This energy mapping
software is based on a multivariate regression analysis of the most
recent Department of Energy Residential Energy Consumption Survey (RECS)
data to identify factors significant in determining total energy use for
each category. The model identified 13 significant variables predicting
84 percent of the variability in home heating energy use (e.g., r2=0.84).
Similarly, the model used 8 significant variables to predict 61 percent
of the variability in home water heating energy use, 9 significant
variables to predict 73 percent of the variability in home cooling energy
use, and 23 significant variables to predict 62 percent of the
variability in home appliance energy use.

[0069]The resulting regression functions were applied to every zip code
using granular default values for every significant variable obtained
from the U.S. Census, further regressions on RECS data for variables not
available in census data, a network of 345 geographically distributed
weather stations, insolation data for every zip code from the National
Renewable Energy Laboratory, NERC subregion emissions factors from the
EPA's e-Grid program, state-level transmission loss data from the DoE's
Energy Information Agency (EIA), and a number of other sources. Results
were independently validated by multiplying estimated median household
energy use by fuel type by the number of housing units in each zip code
and comparing the results on both the state and national level to
residential electricity, natural gas, and fuel oil consumption statistics
from the EIA.

[0070]This approach provides a reasonable idea of the most common home
energy use, fuel type, and appliance use characteristics simply based on
the user's zip code. Each variable is given a zip default value by the
model (e.g., zip code defaults 102). Additionally, a number of variables
(house type, square footage, year built) may be automatically accessed
from county records given the user's home address (e.g., via processing
means 14 accessing the appropriate database A, B, . . . N via a network
16 as shown in FIG. 1).

[0071]User inputs 100 can be provided for the actual values for all
variables by answering a number of questions about the end user's home,
and these values replace the zip code-based defaults 102. The variables
that the users can input include but are not limited to: home type
(house, mobile home, dorm, small apartment, large apartment, condominium,
and the like), total number of rooms, number of heating degree days (base
65) based on the nearest available weather station to the user's zip
code, number of cooling degree days (base 65) based on the nearest
available weather station to the user's zip code, total combined
household income in the past 12 months, number of people in the
household, water heater fuel (electricity 112, natural gas 114, fuel oil
116, or propane 118), water heater size, user does or does not have a
dishwasher, user does or does not have a clothes washer, temperature
setting for wash cycle of the clothes washer, the year the house was
built, total square footage of the house, is someone at home all day on a
typical weekday?, thermostat setting during the day when someone is home,
thermostat setting during the day when no one is home, census division in
which the house is located (based on zip code), age of the main heating
equipment, home heating fuel (electricity 112, natural gas 114, fuel oil
116, or propane 118), material of the house's exterior wall, urban/rural
characteristics of the user's location, the type of air conditioning
system(s), number of rooms cooled by ac, stove fuel (natural gas 114,
electricity 112, or propane 118), number of indoor lights that are on
more than 12 hours a day, number of indoor lights that are on 4 to 12
hours a day, number of indoor lights that are on 1 to 4 hours a day,
presence of outdoor lights, presence of a separate freezer, presence of a
dishwasher, presence of a clothes dryer, presence of a heated water bed,
number of TV sets, presence of a aquarium, cell phone, personal computer,
fax machine, number of refrigerators, age of the main refrigerator,
presence of a heated pool, and the like.

[0072]This information from users will replace the zip code default values
102 that are obtained from the Census data or that are estimated from the
approach described above. The inputs 100 will be plugged into the
regression model, providing more granular user-specific energy use
estimations. Even when users don't provide specific information, the
present invention is still able to estimate energy consumption with
already constructed default values 102 set for each zip code region.

[0073]Further details regarding the operation of the Bottom-Up Energy
Mapping Model 110 and regression models for each energy end-use are
provided below.

Top-Down Bill Disaggregation Model 108

[0074]In cases where there is access to billing data, a Top-Down Bill
Disaggregation Model 108 is used. Instead of inferring how much energy is
used in home heating, cooling, water heating, and appliances based on
home characteristics alone (as is done with the Bottom-Up Energy Mapping
Model 110 described above), the present invention may alternatively use
home characteristics to disaggregate the provided bills into the four
major use categories through a methodology adapted from that used in
producing the RECS category estimates.

[0075]To disaggregate bills into end-use categories, the bills provided in
dollars must first be translated into kilowatt hours, therms of natural
gas, and gallons of fuel oil used (e.g., by the processing means 14 of
FIG. 1). This requires up-to-date energy price data for each user. For
electricity, since this differs on the utility level, a way is needed to
assign each user to a specific utility. Therefore, a database (e.g., one
of databases A, B, . . . N of FIG. 1) of all of the utilities serving
each zip code in the country was created, and a list of potential
utilities for each user can be populated based on their home zip code.
When a user selects a utility, the system is able to look up the latest
monthly rate when it is available (as the Department of Energy's Energy
Information Agency (EIA) only publishes monthly rates for about 500 of
the 3500 utilities in the country, though they include most of the
largest regulated ones). If a monthly rate is not available for the
user's specific utility in the past three months, the system use the
latest monthly average rate for the user's state as a proxy. For natural
gas and fuel oil, state-level price data is taken from the EIA for the
latest month.

[0076]Using energy bills is somewhat complicated due to potentially strong
annual variation in home energy use. While this is not a serious issue
when a full year of past energy bills are available and input into the
system, this may not always be the case, especially for users who have
recently moved or when users are manually inputting bills instead of
simply providing their utility account number so that the billing history
can be electronically accessed. The present invention includes a smart
bill calculator that requires only a single month's bill to be input
(though it allows for multiple months) and, based on the user's state of
residence and heating and cooling equipment and fuel types, estimates
annual electricity, natural gas, and/or fuel oil use. For example, a user
with a window AC unit that lives in Texas would likely have higher summer
electricity use than winter electricity use, and the smart bill
calculator takes this (and other factors) into account when estimating
the annual bill if the user inputs a summer month. Likewise, a user with
a natural gas furnace in, say, New York would have up to an order of
magnitude larger natural gas use in the winter than in the summer, and a
large natural gas bill during the winter would yield a reasonable annual
use estimate based on the model.

Carbon Emissions Calculations

[0077]Because local generation sources are connected to the larger grid,
it is impractical to determine an individual's electricity fuel mix based
on their proximity to specific generators. Rather, the footprint
calculator uses NERC subregion level emission factors based on fuel mix
and generation efficiency data from the EPA's eGRID. Emission factors
also include transmission losses based on data from the EIA and indirect
emissions associated with the fuel-cycle, plant construction, and plant
decommissioning of natural gas, nuclear, oil, coal, solar, wind, biomass,
geothermal, and hydro. Estimates of fuel cycle and plant construction and
decommissioning emissions are based on P. J. Meier's "Life-Cycle
Assessment of Electricity Generation Systems and Applications for Climate
Change Policy Analysis" (2003). Direct emissions from home natural gas
and fuel oil use are calculated based on emission factors from the EPA
and estimated fuel-cycle emissions from Meier (2003).

[0080]For personal vehicles 166, the user inputs 100 regarding the
year/make/model of the vehicle are correlated with a database from the
EPA's National Vehicle and Fuel Emissions Laboratory that provides the
car's fuel efficiency in miles per gallon. Dividing the annual mileage of
the car by the average fuel efficiency in miles per gallon yields gallons
of gasoline consumed (gasoline 167). The system then divides the gallons
of gasoline by the average number of passengers in the car to yield per
person gallons of gasoline. The number of gallons used per year is
converted to pounds of carbon dioxide using conversion factors from the
Technical Guidelines Voluntary Reporting of Greenhouse Gases (DOE, 2006).
For users who know their own vehicles actual miles per gallon, they can
choose to overwrite the default fuel economy of their vehicle with an
actual fuel economy input. This number (in miles per gallon) simply
replaces the value assigned from the EPA year/make/model database.

Flights 168

[0081]Users are given two options for inputting flight data: to provide
specific information about the origin and destination of each flight they
have taken in the past year, or to provide a general estimate of the
number of flights they have taken and their length.

[0082]Users can also input their annual number of short flights (0 to 300
miles), medium flights (301 to 1000 miles), long flights (1001 to 3000
miles), and flights outside the US (extended flights, over 3000 miles).
To convert the number of flights into carbon dioxide emissions, an
average length in miles is assigned to each class of flights: short
flights are 200 miles, medium flights 700 miles, long flights 2000 miles,
and extended flights 5500 miles. In addition, for each flight class there
is an emissions factor in pounds of carbon dioxide per flight mile
derived from the World Resources Institute, GHG protocol initiative. Jet
fuel use (jet fuel 169) is derived based on the carbon intensity of jet
fuel. By multiplying the average flight length by the emissions factor,
and summing for all the flights, the system derives the flight component
168 of the Travel footprint 130.

Other Transportation 170

[0083]The other transportation component 170 of the travel footprint 140
includes vehicle rentals, public transport, taxis, and the like.

Vehicle Rentals

[0084]Users can further refine the "driving" component of the Travel
footprint 130 by describing the number of days the user rents a car each
year, and specifying what type of car is typically rented (choices may be
small car, midsize/sedan, minivan, SUV/pickup, hybrid SUV, and hybrid
car). To calculate the associated consumption of gasoline, the number of
rental car days is multiplied by an average daily driving load of 50
miles (number based on rental packages from various rental car
companies). This yields annual rental car miles. The system then divides
by the average fuel efficiency for a car in the class (derived by
observational studies of EPA mileages of various cars in the class) to
yield annual gallons of gasoline consumed for rented cars.

Public Transport and Taxis

[0085]The user can also refine the Travel footprint 130 by answering
questions or inputting information to define the "other" component.
Specifically, the user can input how much the user spends on
busses/taxis/commuter trains/subways, train travel between cities, and
ferries/water taxis. For each of these three categories, there are
corresponding multiplication factors that relate user-inputted dollars
spent to both emissions of carbon dioxide based on data from Carnegie
Mellon University Economic Input-Output Lifecycle Assessment (EIOLCA)
program. By multiplying the dollars spent by the respective EIOLCA
multiplication factor, and summing across the three spend categories, the
system determines the "other" component of the Travel footprint.

Work Footprint 132

[0086]The Work footprint 132 may be calculated in a number of different
ways based on the user's occupation. Users get to choose from the
following: [0087]"I work at home." [0088]"I work in a building that
manufactures stuff." [0089]"I work in a building that doesn't manufacture
stuff." [0090]"I am a student or teacher." [0091]"I am unemployed."

[0092]Based on the user's response, the user is directed down one of a
number of paths, described below. The user is also asked to indicate the
zip code in which he/she works, since some users may live in one zip code
and commute to work in another.

"I Work at Home" or "I am Unemployed"

[0093]For both of these responses, a user's work footprint is zero. An
unemployed user does not work, so by definition must have a work
footprint of zero. For a user that works at home, the fuel consumed in
the course of this work will be included in the bills entered in the Home
function questions, and will thus be part of the Home function. In cases
where users do not enter bills, the default home energy use simulations
are scaled to estimate extra energy use associated with working at home.
However, it should be appreciated that the a user who works at home could
input only information associated with a home office (that is not already
included in the home footprint) to the extent possible, in order to
obtain an indication regarding the portion of the overall footprint
attributed to the home office.

"I Work in a Building that Doesn't Manufacture Stuff"

[0094]If a user indicates that she works in a non-manufacturing commercial
field, the user is prompted to describe the type of building he/she works
in with the following choices: school, supermarket or grocery store,
restaurant, hospital, doctor or dentist office, hotel or motel, retail
store, professional or administrative office, social space, police or
fire department, place of religious worship, post office or copy center,
dry cleaners/laundromat/beauty parlor, auto service or gas station,
warehouse or storage facility. Each of these responses corresponds to one
of the building types described in the EIA's Commercial Building Energy
Consumption Survey (CBECS, 2003). This survey provides per worker
electricity 158 and natural gas 160 consumption for each of these
building types.

[0095]CBECS also assigns average per worker consumption of electricity and
natural gas based on the census of the commercial building. A census is a
geographical division, with nine censuses in the nation, each consisting
of a varied number of states with a similar geography. For each census, a
multiplication factor is derived that relates average consumption of
electricity and natural gas to average consumption for the entire nation.
As such, when a user reports his state, the system can assign him to a
census and multiply the per worker consumption based on his building type
by the census multiplication factor. This outputs a census- and
building-modified per worker consumption of electricity 158 and natural
gas 160. Since these are the only required inputs, these physical units
of fuel can be converted to emissions of carbon dioxide and energy
consumption using the same NERC subregion-level multiplication factors
described earlier in the Home function.

[0096]Although these questions are enough to output an estimated Work
footprint 132, the user will be able to refine his Work footprint 132 by
providing information for any or all of the following: [0097]The square
footage of the building [0098]The age of the building [0099]The number of
floors [0100]The number of people working in the building [0101]The hours
of operation for the building [0102]The building's exterior material

[0103]The CBECS survey provides per worker consumption of electricity 158
and natural gas 160 for workers in the different building characteristics
outlined in each of these. For each response the system generates a
multiplication factor that relates the building type with the overall
average, and then multiplies it by the census- and building-modified per
worker average. Since these are independent multiplication factors, the
system can just sequentially multiply by them in any order. Moreover, if
a user does not know the response to a question, or leaves it blank for
any other reason, the system does not multiply by any factor and the per
worker consumption does not change.

"I Work in a Building that Manufactures Stuff"

[0104]If a user indicates that they work in a building that manufactures
things, the user is then prompted to describe the manufacturing subsector
of the facility. The choices for this input are: food, beverage and
tobacco products, textile mills, textile product mills, apparel, leather
products, wood products, paper, printing-related support, petroleum and
coal products, chemicals, plastics and rubber products, nonmetallic
mineral products, primary metals, fabricated metal products, machinery,
computer and electronic products, electrical equipment, transportation
equipment, furniture and related products, miscellaneous. Each of these
categories corresponds to a subsector in the EIA's Manufacturing Energy
Consumption Survey (MECS, 2002). MECS gives the total consumption,
consumption per employee, electricity consumption, and natural gas
consumption, broken down by region (there are four regions in the nation,
and each comprises at least two censuses). From this data, the system can
derive per worker electricity 158 and natural gas 160 consumption for
each region, and assign the user to one of the regions by knowing the
user's work state. The system can then adjust the per worker numbers to
account only for non-process consumption. In other words, the system does
not assign to the user the electricity and natural gas that is used in
the manufacturing process, but only the electricity and natural gas that
is used for the benefit of the facility's workers, such as for HVAC,
lighting, on-sight transportation, etc. Thus, with only the worker's
state and subsector, the present invention can output per worker
consumption of electricity 158 and natural gas 160 along with the overall
Work footprint that is the sum of these two.

[0105]As with other footprint components, a user can return and refine the
Work footprint 132 by answering more questions about the user's
manufacturing job. For example, within certain subsectors, there are more
specific industries. For instance, if a user selects the subsector
"food", the user may refine his industry to wet corn milling, sugar,
fruits and vegetable canning, or I don't know/none of these. By selecting
an industry, a user is assigned to a more specific category on the MECS
survey, although the same data is available for the industry and it is
manipulated in the same way. If a user selects "I don't know/none of
these", the system simply carries the calculation forward with the data
from subsector rather than the more specific industry data. Not all
subsectors have industries within them, so for those subsectors there is
no corresponding question or input regarding the specific industry.

[0106]In addition, a user may also be asked to describe the number of
workers in the user's manufacturing facility. From the MECS survey, the
system can generate multiplication factors within each industry and
subsector relating consumption for each facility size to the average
consumption across all facilities. So, if a user is able to select the
facility size, the system can multiply the consumption of electricity 158
and natural gas 160 by this multiplication factor to further refine the
Work footprint 132. Once again, the system can convert to carbon dioxide
emissions using the NERC subregion-level conversion factors used above.

"I am a Student or Teacher"

[0107]If the user selects this statement, they are further asked to
clarify whether they are a student or teacher, and in what level of
schooling (kindergarten, elementary school, middle or high school,
college or graduate school). Based on the response to this question,
there a few pathways the system can take.

[0108]If a user is a teacher in kindergarten through high school, they are
actually treated in the same way as those users who "work in a building
that doesn't manufacture stuff." In this pathway, outlined above, the
user is normally prompted to describe the user's building. However, in
the case of teachers, the system can assign the building type to
"school." Using this response, and the work state, the system can utilize
CBECS data to yield per worker consumption of electricity 158 and natural
gas 160.

[0109]In addition, as with the non-manufacturing questions outlined above,
the user can refine the footprint by answering questions to describe the
school's square footage, construction year, number of floors, number of
employees, weekly operating hours, exterior wall material, and the like.
The resulting CBEC S-derived multiplication factors can refine the user's
Work footprint.

[0110]This pathway also utilizes the same CBECS pathway utilized above and
in non-manufacturing buildings. However, that data outputs electricity
and natural gas per employee, so the system adds another multiplication
factor to the student pathway which accounts for the larger number of
students as compared to just workers. This larger number will decrease
the per student consumption of electricity and natural gas, as the total
consumption is spread out over a wider range of students. Here a
conscious decision is made to assign less consumption to students than
teachers, as teachers are assigned per worker values, while students are
assigned a value that is per (worker+student). This decision was made
because students spend less time in the school than teachers do, and have
a less direct financial stake and smaller choice to be in the school in
the first place.

[0111]As above, the system can take the CBECS data for education buildings
in the appropriate census division (based on user state). Now, the system
multiplies by a factor relating number of worker to total number of
workers and students. This factor is derived from the National Center for
Education Statistics, which provides student to teacher ratios for
kindergarten, elementary school and secondary school, as well as student
to administrative staff ratio, all broken down by state. By combining
these data, the system derives a ratio of workers to workers and
students, which when multiplied by the per worker electricity and natural
gas consumption, provided electricity 158 and natural gas 160 consumption
per workers plus students. These outputs, electricity 158 and natural gas
160, are the subcategories for a student's footprint, and when summed,
provides the overall Work footprint 132.

[0112]As with the non-manufacturing questions outlined above, the user can
refine the footprint by answering questions to describe the school's
square footage, construction year, number of floors, number of employees,
weekly operating hours, exterior wall material, and the like. The
resulting CBECS-derived multiplication factors can refine the user's Work
footprint 132.

"I am a Student or Teacher in College or Graduate School"

[0113]Students and teachers in college or grad school are treated as
equals, in contrast to students and teachers at any other level of
schooling. The reasoning that there is no difference between students and
teachers in college relates to the fact that both spend comparable
amounts of time in the school buildings, and both choose to be in the
buildings for either current employment or training for potential future
employment. In this category, published emissions inventories from dozens
of colleges in the United States were researched, inventories that took
into account all buildings on a university campus. These college reports
were grouped into four regions, and the average carbon dioxide emissions
per community member at the college was calculated. As such, a student or
teacher in college or graduate school is assigned one of these average
footprints, which are subsequently broken down into the subcategories of
electricity, on-campus sources, and other.

Shopping Footprint 134

[0114]The Shopping footprint 134 is meant to capture the indirect
emissions associated with the manufacture and distribution of the
products the end user purchases on a daily basis. To break down a typical
user's spending into discrete categories, the system begins with 2005
consumer spend data from the U.S. Bureau of Labor Statistics (BLS) (or
such data as may be updated from time to time), which details average
spending by Americans in 13 broad categories: [0115]Food and alcohol
178, which includes food at home, food away from home, and alcoholic
beverages; [0116]Housing 180, owned dwellings, rented dwellings, other
lodging, utilities fuels and publics services (not included), household
operations, household supplies, household furnishings and equipment;
[0117]Apparel and services; [0118]Transportation, which includes vehicle
purchases, gasoline and motor oil (not included), other vehicles
expenses, and public transportation (not included); [0119]Healthcare 182;
[0120]Entertainment; [0121]Personal care products and services;
[0122]Reading; [0123]Education; [0124]Tobacco products and smoking
supplies; [0125]Miscellaneous (not included); [0126]Cash contribution
(not included); and [0127]Personal insurance and pensions, which includes
life and other personal insurance and pensions and social security.

[0128]For each of these categories, the description from the BLS survey
was used to assign a reference category from Carnegie Mellon University's
EIOLCA program. This process provides multiplication factors to convert
the dollars spent in each of these categories to the corresponding
emissions of carbon dioxide and energy consumption. Certain categories
were omitted: utilities fuels and public services were omitted because
these are included in the Home footprint 128, education was omitted
because it is included in the student's Work footprint 132,
gasoline/motor oil and public transportation were omitted because these
are included in the Travel footprint 130, and miscellaneous and cash
contributions were omitted because of difficulty in defining these for
the user and in assigning an EIOLCA reference category. Thus, the four
primary subcategories used to determine the hopping footprint comprise
food and alcohol 178, hotels and housing 180, healthcare 182, and other
183 (which may comprise some or all of the remaining items from the 13
categories referenced above not included in the Home footprint 128, the
Work footprint 132, or the Travel footprint 130).

[0129]In order to derive a Shopping footprint 134, the system multiplies
the amount spent in each spend category (obtained via user input 100) by
the corresponding EIOLCA multiplication factor and a value to adjust for
inflation based on the BLS Consumer Price Index. To assign spending in
each category without asking the user, the system utilizes data from the
BLS survey, which provides average consumer spending for each of these
categories, broken down by income range of the consumer. This is based on
the user's household's combined annual income or, when not provided, the
U.S. average household income for 2005. Based on the user's reported
income, the system can assign the average spending for the user's family
in each of the spend categories.

[0130]For example in determining the footprint for the food and alcohol
subcategory 178, the user is also asked to input whether he/she is a
vegetarian, vegan, or omnivore. BLS survey data is used to estimate food
expenditure in each major food category (cereals and breads, chicken and
fish, red meat, dairy products, fruits and vegetables, and sugars and
sweets) based on income level. The estimated calories consumed are
derived for each food type based on the average calories per dollar for
that food type. For users who are vegan, the system replaces all red
meat, chicken/fish, and dairy calories with an equal division of grains
and breads and fruits and vegetable calories. For vegetarian users, the
system divides red meat and chicken/fish calories equally between fruits
and vegetables, grains and breads, and dairy.

[0131]If a user chooses to refine the Shopping footprint 134, the user may
input the specific amount of spending in each of the subcategories 178,
180, 182, and 183. There is also an additional subcategory, credit card
spending, which may be incorporated into the other subcategory 183 since
purchasing any product with a credit card as opposed to cash leads to
additional emissions of carbon dioxide and energy consumption. To allow
maximal flexibility for users, they can enter weekly, monthly, or yearly
spending for each of the spend categories, and the system can annualize
these numbers.

Energy End-Use Determination with Bottom Up Energy Mapping Model 110

[0132]As discussed above, Bottom Up Energy Mapping Model 110 specifies
regression models for each energy end-use. This regression analysis
consists of four major residential energy end-use categories: space
heating 124, water heating 126, cooling 120, and appliance 122, but can
of course be expanded to include other categories as would be apparent to
those skilled in the art. In accordance with the present invention, a
statistical regression model is created for each category with the
micro-data files from The Residential Energy Consumption Survey (RECS) in
2005 (or as updated from time to time). This survey collected data from
4382 households randomly sampled through a multistage, area-probability
design method to represent 111.1 million U.S. households, the Census
Bureau's statistical estimate for all occupied housing units in 2005.
Each sampling weight value was used as weighting factor for the analysis.

[0133]Ordinary Least Square (OLS) method was used with predictor variables
such as energy price, household characteristics, housing unit
characteristics, geographical characteristics, appliance ownership and
use pattern, and heating/cooling degree-days. Dependent variables of the
four regressions were natural log values of per household energy use for
heating, water heating, appliance, and cooling. The model can be
formulated as

ln E j = β j 0 + i β ij
X i , RECS + j , ( 1 ) ##EQU00001##

where j indicates the four categories of heating 124, water heating 126,
cooling 120, or appliance 122, Ej is total annual energy consumption
for each end use, and Xi,RECS means variable Xi (e.g. housing
type) whose value is from the RECS dataset. This RECS notation is used
because later the system also uses Xi values from other datasets for
prediction purpose. The dependent variables Ej are aggregation of
energy use per fuel per end-use which the Energy Information
Administration (EIA) estimated from the total fuel uses per household.
Each means

where NG means natural gas, EL electricity, FO fuel oil, and LP propane.
The regressions results for selected major variables are shown in Table 2
below. These regression models will be used to predict household energy
use with more granular data source.

Leveraging Census Data to Achieve High Geographical Resolution

[0134]Since a goal of the present invention is to estimate per-household
energy use in a geographical resolution in as granular a manner as
possible, the resolution in the RECS dataset, which is the U.S.
division-level, was not satisfactory. Instead, the system makes use of
the U.S. Census 2000 dataset which contains 5-digit zip code level
information for many independent variables used in the regressions, such
as household characteristics. In terms of weather data, the closest
weather station from the center of each zip code area is selected, out of
hundreds of weather stations scattered nationwide, and the 5 year average
values of the climate variables from that station are used.

[0135]Then, the independent variables in the four main regressions can be
divided into two groups A and B: A with variables Xai whose values
exist in both RECS and Census datasets, and B with variables Xbi
that exist only in RECS dataset. For example, the group A will include
information about years when the structure was built, heating fuel types,
housing types, number of rooms, or household income, while the group B
contains number of windows, housing wall types, or appliance ownership
and use patterns. Because all these variables are used in the main
regression models, the system needs to have proxies for the variables in
the second group in order to predict zip code level per-household energy
use.

[0136]For this purpose, separate sub-regressions were run with the
variables in the group A to estimate the variables in the group B. That
is,

X bk , RECS = γ k 0 + i γ ik
X ai , RECS + δ k , ( 3 ) ##EQU00003##

Then, the Census and the weather data X.sub.a,census can be plugged into
these sub-regression models to predict {circumflex over (X)}bis for
each zip code area.

X bk ^ = γ k 0 ^ + i γ ik
^ X ai , census , ( 4 ) ##EQU00004##

These {circumflex over (X)}bis which will in turn be used to predict
zip code level energy estimates Ej

In the equation (4), for dichotomous variables like ownership variables,
logistic regressions are used to obtain probabilities of owning each
appliance. These probability outputs enable the system to model a
probabilistic household in each zip code. For example, a representative
household may have 0.6 units of electric water heater and 0.4 units of
natural gas one.Rearranging the estimated end-use energy consumption to
obtain energy use per fuel

[0137]As a way of validating this approach, the estimated nationwide
consumption of each fuel is compared with the actual statistics released
from the EIA every year. To estimate nationwide fuel consumption, the
results from above which were per-household energy use are rearranged for
different end use categories.

[0138]First, it is assumed that all cooling energy is from electricity. So
all Ecooling is added to electricity use. Second, water heating and
space heating energy, Ewater and Eheating, are divided into
four different fuel types depending on the coefficients of the
regressions and the percentage of households using each fuel in the zip
code area. Since the model is log-linear, each coefficient β of a
dichotomous variable can mean, when β is small, 100β% change in
the dependent variable (since e.sup.β≈1+β). For
example, according to Table 2 below, "Fuel oil furnace" has the
coefficient 0.280, which means households using fuel oil heating
equipment use about 32.3% more heating energy than others with everything
else equal. From this consideration, the system can disaggregate each
end-use energy for a representative household to obtain energy use per
each fuel type by the following equation. For a particular zip code area
j, heating energy from gas for the representative household is:

which can be multiplied by total number of households to estimate total
heating energy from gas in the area. Here rj,i means the proportion
of households using fuel i as the main heating fuel in an area with zip
code j. The same approach is applicable to all other fuel types used for
water and space heating.

[0139]Third, since appliance energy is used for various purposes, the
system cannot divide it as simply as the method above. Lighting or
refrigerator is entirely driven by electricity, while energy use for
stove, oven, pool, spa, dryer, and grill may come from either gas or
electricity. Since a majority of households (according to the RECS data,
it is about 54%.) use only electricity for all appliance use, the system
cannot treat all the households in the same way when modeling other fuel
usage for appliances. Instead, first a regression model is built only
with households using not only electricity for appliances to estimate the
ratio {circumflex over (r)}e of electricity to total appliance
energy. Second, the probability pj of using 100% electricity for
each representative household is estimated. For this, a logistic
regression can be run with a dependent variable of whether each household
uses 100% electricity for appliances or not. With this probability an
expected ratio E[re] of electricity use can be calculated for
appliances in the region.

E[re]=pj1+(1-pj){circumflex over (r)}e, (7)

Specific Regression Outputs

[0140]From the log value that is obtained from the regression models,
actual estimated energy can be obtain by:

{circumflex over (Ej)}=exp(RMSE2/2)exp(1{circumflex over (n
Ej))}

The scaling value exp(RMSE2/2) is needed when using a log-linear
model because without it the expected value of Ej is systematically
underestimated (Wooldridge 2006: p 219). RMSE means root mean square
error of each model.

[0141]The full lists of significant variables and coefficients for each
regression with the descriptions about the variables are set forth below.
Regressions are run by STATA 10.0 software.

[0142]In order to decompose a total energy bill (e.g., electricity bill
104 or natural gas bill 106) to acquire energy use for each end use, a
linear model is needed, which has the additive relationship between
independent variables and the final variable, which is total energy
consumption. In this method, total consumption of a certain type of fuel
for any single household will be expressed linearly as

Efuel=E.sub.appl+Eheating+Ewater+Ecooling, (1)

Each sub-component of total fuel use will be the estimates for each end
use consumption. However, the system cannot run a simple linear
regression because the error term in the model does not satisfy the
homoskedasticity condition of least square method, which means that the
variances of error terms are not a constant across all household samples.
To account for this problem, the EIA notes that from its previous
analysis it discovered that with a non-linear model

Efuel,i1/4={E.sub.appl,i+Eheating,i+Ewater,i+Ecoo-
ling,i}1/4+ε, (2)

where i means i-th household, the error term ε is more normally
distributed and has approximately a constant variance (Latta, 1983). This
nonlinear least square method is adopted, which will minimize
ε2 in the model. Each term on the right side can be
separated from the others by using indicator variables specifying each
term such as fueltype5 or aircond. Each respectively denotes whether
users have electricity as a main fuel and whether users have
air-conditioning or not. This non-linear regression will provide four
sub-equations for the four terms on the right side. Before using the
results from the four sub-equations, provided that the system already has
the total energy consumption values, it can normalize each term by the
sum of all terms in the equation (1) to avoid over or underestimation of
the total values. It can be shown as

where Efuel,i is the total annual bill for household i and fuel type
fuel, {tilde over (E)}j,i means energy use estimation from the
sub-equations and {tilde over (E)}j,i means the final scaled
estimation for the end use j.

[0143]For example, from this method, the sub-equations acquired for
electricity bill 104 decomposition are

[0147]FIG. 3 shows a flowchart of an example embodiment of the Personal
Energy Advisor Software. The processes described in the FIG. 2 flowchart
may be implemented on the system shown in FIG. 1.

[0148]The starting point for the Personal Energy Advisor is the initial
footprint categories determined in connection with the Energy Mapping
Software described above. Accordingly, in FIG. 3, the Initial Home
Footprint 128, the Initial Travel Footprint 130, the Initial Work
Footprint 132, and the Initial Shopping Footprint 134 correspond to the
Home Footprint 128, the Travel Footprint 130, the Work Footprint 132, and
the Shopping Footprint 134 of FIG. 2. Further, at least initially, the
initial greenhouse gas emissions and energy use estimate 136 of FIG. 2
will correspond to the current user footprint 136 of FIG. 3. Sub-category
reductions may be based on user-selected actions or purchases in
connection with the initial Home, Travel, Work and Shopping Footprint
values to provide a Current Home Footprint 140, a Current Work Footprint
142, a Current Travel Footprint 144, and a Current Shopping Footprint
146. The Current User Footprint 136 may then be updated by subtracting
the sub-category reductions from the initial (or previously determined)
Current User Footprint 136 in order to determine the impact of a selected
or proposed user action or purchase on the overall greenhouse gas
emissions and energy usage of the end-user. Such actions or purchases may
be input via user interface 12 of FIG. 1.

[0149]For example, in connection with the Initial Home Footprint 128, user
inputs may be received regarding an action or purchase (or proposed
action or purchase) in connection with the user's space heating, water
heating, cooling, and appliance information. The system will then
determine an appropriate reduction for the action or purchase (e.g., one
or more of space heating reductions 150, water heating reductions 152,
cooling reductions 154, appliance reductions 156), which can then be
subtracted from the initial values determined by the Energy Mapping
Software for space heating 124, water heating 126, cooling 120, and
appliance 122 (or those values as previously modified by the Personal
Energy Advisor Software in connection with previously entered actions
and/or purchases) to provide the Current Home Footprint 140.

[0150]In connection with the Initial Work Footprint 132, user inputs may
be received regarding an action or purchase (or proposed action or
purchase) in connection with the user's electric 158 or natural gas usage
160. The system will then determine an appropriate reduction for the
action or purchase (e.g., one or more of electric reductions 162, natural
gas reductions 164, or the like), which can then be subtracted from the
initial values determined by the Energy Mapping Software for electric 158
and natural gas 160 (or those values as previously modified by the
Personal Energy Advisor Software in connection with previously entered
actions and/or purchases) to provide the Current Work Footprint 142.

[0151]For the Initial Travel Footprint 130, user inputs may be received
regarding an action or purchase (or proposed action or purchase) in
connection with the user's vehicle, flight, or other transportation
information. The system will then determine an appropriate reduction for
the action or purchase (e.g., one or more of vehicle reductions 172,
flight reductions 174, and other transportation reductions 176), which
can then be subtracted from the initial values determined by the Energy
Mapping Software for vehicles 166, flights 168, and other transportation
170 (or those values as previously modified by the Personal Energy
Advisor Software in connection with previously entered actions and/or
purchases) to provide the Current Travel Footprint 144.

[0152]In connection with the Initial Shopping Footprint 134, user inputs
may be received regarding an action or purchase (or proposed action or
purchase) in connection with the user's food and alcohol, hotels and
housing, healthcare, or other purchasing information. The system will
then determine an appropriate reduction for the action or purchase (e.g.,
one or more of food and alcohol reductions 184, hotels and housing
reductions 186, healthcare reductions 188, and other purchases reductions
190), which can then be subtracted from the initial values determined by
the Energy Mapping Software for food and alcohol 178, hotels and housing
180, healthcare 182, and other purchases 183 (or those values as
previously modified by the Personal Energy Advisor Software in connection
with previously entered actions and/or purchases) to provide the Current
Shopping Footprint 146.

[0153]The Current Home Footprint 140, Current Work Footprint 142, Current
Travel Footprint 144, and Current Shopping Footprint 146 can then be
summed to provide the Current User Footprint 136. It should be
appreciated that where no reduction input information is received for a
particular category or sub-category, the footprint attributable from that
category or sub-category will remain as initially determined in
connection with the Energy Mapping Software discussed above or as
previously modified by the Personal Energy Advisor Software.

[0154]Unlike other calculators, such as Yahoo! Green or An Inconvenient
Truth Calculator, which are limited to providing outputs that apply
across individuals in a zip code, state or even nation, the Personal
Energy Advisor can yield reliable, market-leading estimates that apply
specifically to the end user and no one else. The Personal Energy Advisor
provides the foundation for an innovative kind of personalized e-commerce
and conservation experience capable of dramatically spurring the
transition to a sustainable future. The system makes it possible for
energy efficiency and e-commerce to take into account an individual or
organization's demographic, psychographic and energy usage
characteristics, lifestyle or business habits, and purchasing decisions
to determine the behavior, action or product that maximizes the user's
end goal, including maximizing carbon dioxide emissions reductions,
maximizing dollar savings, maximizing the savings of particular
resources, maximizing the cost per carbon dioxide reduced ratio, and
others.

[0155]Personal Energy Advisor is both a tool to assist consumers and
organizations in making decisions about actions and purchases in their
everyday lives, as well as a method for collecting data regarding such
decisions. Certain representative features of the Personal Energy Advisor
are listed below. This list is not intended to be exhaustive:

[0156]Algorithms may output: (1) energy savings as a rate or absolute
value; (2) CO2 emissions and other greenhouse gas reductions as a rate or
absolute value; (3) investment cost/annual dollar savings as a rate of
absolute value; and (4) resource savings associated with any of the other
following outputs relevant to the behavior, action or purchase--including
water, gasoline, electricity, paper, natural gas, heating oil, and
others--as a rate or absolute value;

[0157]Algorithms rely upon on user-specific equations and variables--that
is, they may be geared towards the individual choices (inputs stemming
from actions taken or products purchased) of the user and differentiate
between such choices to yield distinct outputs for the particular user.
This includes the ability of the user to replace default values used in
the calculation.

[0158]Algorithms and the databases undergirding such algorithms are
adapted to provide sufficient flexibility to meet varying time and
accuracy budgets of users. Thus, the user has the ability to input as
little or as much information as it elects.

[0159]Because the material conditions of each purchase are far too varied,
calculation methodology cannot be described across all potential
behavioral, action and purchase decisions, though certain principles and
practices are ever present. A few descriptions may help clarify the
principles and practices expressed through the Personal Energy Advisor.

[0160]For example, under the cooling reductions 154 of the Home Footprint,
a determination of the impact of the user's decision to install a ceiling
fan in a room instead of using a window air conditioning unit begins by
describing the benefits of such an installation (e.g. a ceiling fan can
make the room feel up to 7 degrees cooler) and sources of any data relied
upon or manipulated by the system (in this case, data from the EPA and
Columbia University). The system then asks the user to input the comfort
temperature above which they wish to cool their room (a default value of
72 degrees F. applies if the user elects not to input a value), the
number of hours they cool their room per day on days above their comfort
temperature (default value of 9, derived from Columbia University data),
the energy efficiency ratio of the window air conditioning unit (default
value of 9.8, representing the market average) and the cooling capacity
of the window air conditioning unit (default value of 10,000 BTU
representing the market average).

[0161]The system then uses the user's zip code and queries a database in
the Energy Mapping Software to retrieve the climate division associated
with that zip code. It then proceeds to examine a list of 345 weather
stations located in uniquely characterized climate division regions all
around the United States to determine which one is associated with the
user's zip code. It then retrieves the temperatures for every day over
the last five years at the weather station closest to the user's address
to determine the number of days less than two degrees, two to four
degrees, four to seven degrees, or more than seven degrees above the
user's comfortable temperature. These values correspond to the number of
days the user would have to run the fan on low, medium, or high
respectively, or to use an air conditioning unit instead of the fan, as
occurs when the temperatures are more than seven degrees above the
comfort temperature and the fan cannot provide enough cooling to be
viable. The number of days in each of these categories is divided by five
to determine the average number of days per year for each.

[0162]The system may next uses the energy use of various replacement fans
in a list of products created to generate user-specific results for a
number of different competing products. For each fan, the carbon dioxide
emissions reduction, electricity use reduction, cost, and savings
(relativized to the cost of using the air conditioning unit) are
calculated. The electric reduction 162 is then calculated based on the
average hourly electricity use of the user's current air conditioning
unit minus the expected electricity use of the various replacement fan
options on low, medium, and high based on their expected use pattern from
the daily temperature data described above. The carbon reduction is
calculated based on the electricity savings and the direct and indirect
emission factors of the subregional grid in which the user resides via
the same methodology described above in the home footprint component 128
of the footprint calculator description set forth above in connection
with FIG. 2. Savings are calculated based on the electricity reduction
and the latest monthly electricity prices for the user's state of
residence or utility provider. Finally, the system uses the distribution
of home heating degree days from the user's climate division across
different months to estimate monthly dollars saved and carbon reduced.
The user may elect to purchase a fan based on the associated carbon
reduction, dollar savings, and cost associated with each.

[0163]Another example of a decision is to purchase a low-flow showerhead
and the associated water heating reductions 152. To calculate the energy,
water, carbon dioxide emission, and dollar savings associated with
switching from a standard showerhead to a low-flow showerhead, the system
takes into account the number of minutes per day the user spends
showering, the fuel type of the user's current water heater (electricity,
oil, or natural gas), the water heater type (storage or instantaneous),
and the water heater age, all of which have default values representing
average behaviors or product characteristics. The user may rely upon
default values for shower temperature, water heater temperature, tap
water temperature, and flow rate of their current showerhead based on
market averages for these values. The user can elect to alter any of this
information to produce a more reliable estimate by notifying the system
of its water heater fuel, type, age and so on. Default values nonetheless
provide a reasonably reliable estimate of actual values.

[0164]The system then determines the number of gallons of hot water (from
the water heater) and cold water (from the tap) used in the user's daily
shower based on the duration, temperature, water heater temperature, tap
water temperature, and showerhead flow. It then determines the energy use
per gallon of hot water used based on the water temperature and energy
factor of the user's water heater queried from a manufacturer's database
using the model number or other form of brand and model identification.
Finally, it multiplies the energy use per gallon by the number of gallons
of hot water used per year to determine the energy use of the user's
current showerhead. The direct carbon dioxide emissions associated with
this energy use are determined by multiplying the energy use by the
carbon intensity of the user's electricity fuel mix and water heater fuel
obtained from the EMS. The indirect carbon dioxide emissions associated
with general water use are calculated using economic input-output
lifecycle assessment tables.

[0165]The user is then presented with a number of potential product
choices, each with an associated carbon dioxide emissions reduction,
energy savings, water savings, cost savings, and product price. The
system determines these values for each of the replacement showerheads by
running simultaneous simulations and determining the difference between
the current showerhead and the various potential replacements.

[0166]The present invention also includes a Personal Energy Advisor
Savings Planner, which allows the user to set a goal of saving a
particular amount each month on a fuel bill of their choice (electricity,
natural gas, fuel oil, or propane) or across all bills. The user is
provided with a list of recommended actions to meet this goal dynamically
generated based on which actions have the highest cost-benefit ratio,
with the user's choice of upfront cost preference (low, medium, high)
affecting the discount rate used in creating the priority list. Each user
receives distinct recommendations based on their initial energy use
characteristics as determined by the Energy Mapping Software, as well as
numerous other demographic and psychographic characteristics. Users can
choose to remove suggested actions they do not want to undertake and are
provided with a new list that fills in the removed action with one or
more replacement actions. Users can also personalize suggested actions
with specific energy use behavioral characteristics, which will also add
or remove other actions from the recommendation list as needed to
maintain the user's stated savings goal.

[0167]Thus, the Personal Energy Advisor is a personalized greening advisor
that enables its users to determine precisely how much different
behaviors, actions and products will affect climate change and their
respective spending budgets. The examples provided herein are
representative only. The Personal Energy Advisor currently includes
hundreds of distinct behaviors, actions and purchases at the consumer and
organizational level spanning thousands of products and many thousands of
inputs.

[0168]The algorithms and databases that constitute the Personal Energy
Advisor are too numerous to mention herein, but five examples will
illustrate to those skilled in the art how the method is implemented. The
following models relate to the impact of: closing your window blinds
during the summer; running fewer clothes washer cycles by fully loading
the washer; lowering the water temperature for dishwashers; replacing
single pane windows with double pane ones; and cleaning lint filters in
clothes dryers before each load.

[0176]Tavg,i=Average outdoor temperature for day i measured from the
closest weather station from the user's zip code z [° F.] (Note 1)

R before = Radiation heat gain through
the window before closing the
blinds [ BTU / year ] = ( A north
summer e north + A south summer e south +
A east summer e east + A west summer e west
) g window n cd ##EQU00010##

[0177]edirection=Daily average radiation per unit area on a vertical
wall [BTU/ft2]/day] (Note 2)

[0198]vtub=Tub capacity of clothes washer [ft3]n=Times that
users will reduce by this commitment [cycle/week]aw=Age of current
water heater [year]ac=Age of current clothes washer
[year]Tw=Target temperature of water heater [°
F.]mwash=Operation mode of wash cycle (hot, warm, or
cold)mrinse=Operation mode of rinse cycle (hot, warm, or cold)

4) Assume that clothes washers use the same amount of water for wash and
rinse cycles.

Default Values for User Inputs:

[0204]vtub=3.5 [ft3]n=2 [cycle/week]aw=10 [year]ac=6
[year]

Tw=135 [° F.]

Mwash=hot

[0205]Mrinse=warm

Monetary Savings

[0206]Net Annual Monetary Savings[$/year]=NEPi,fuel

where Pi,fuel=Price of fuel (gas, oil, or electricity) in the region
where user i lives.

Carbon Savings

[0207]Net Annual Carbon Savings[lb/year]=NEefi

where efi Emission factor of electricity in the region where user i
lives.

Notes

[0208]1. Energy Consumption of Major Household Appliances, Trends for
1990-2005, Natural Resources Canada [0209]2. Data from EPA Energy Star
and The Effect of Efficiency Standards on Water Use and Water Heating
Energy Use in the U.S.: A Detailed End-use Treatment by Jonathan G.
Koomey, Camilla Dunham, and James D. Lutz, 1994

3. "Dish_Washer_Temperature"

Description of Measure

[0210]Lowering the water temperature for dishwashers

Inputs

[0211]Tbefore=Original water temperature of dishwasher [° F.]

Tafter=Target water temperature of dishwasher [°
F.]ad=Age of the old dishwasher to be replaced [year]n=Average times
of dishwasher use per week [cycle/week]aw=Age of current water
heater [year]Tw=Target temperature of water heater [° F.]

[0218]4) Assume that efficiency of boost heater inside dishwashers can be
considered as 1.

Default Values for User Inputs:

Tbefore=140 [° F.]

Tafter=120 [° F.]

[0219]ad=7 [year]n=4 [cycle/week]aw=10 [year]

Tw=135 [° F.]

Monetary Savings

[0220]Net Annual Monetary Savings[$/year]=NEPi,fuel

where Pi,fuel=Price of fuel (gas, oil, or electricity) in the region
where user i lives.

Carbon Savings

[0221]Net Annual Carbon Savings[lb/year]=NEefi

where efi=Emission factor of electricity in the region where user i
lives.

Notes

[0222]1. Energy Consumption of Major Household Appliances Shipped in
Canada--Trends for 1990-2005, Natural Resources Canada,
http://oee.nrcan.gc.ca/Publications/statistics/cama07/index.cfm [0223]2.
Data from EPA Energy Star and The Effect of Efficiency Standards on Water
Use and Water Heating Energy Use in the U.S.: A Detailed End-use
Treatment by Jonathan G. Koomey, Camilla Dunham, and James D. Lutz, 1994
[0224]3. Regression based on data from "Energy and Water Use
Determination" by U.S. DOE Energy Efficiency and Renewable Energy (EERE),
http://www.eere.energy.gov/buildings/appliance_standards/residential/pdfs-
/home_appliances_tsd/chapter--6.pdf

C.sub.season,d=Conduction heat gain or loss through the double pane window
during that season [BTU/year]A=Total window area
[ft2]=Anorth+Asouth+Aeast+Awest
Tavg,i=Average outdoor temperature for day i measured from the
closest weather station from the user's zip code z [° F.] (Note 1)

R season , s = Radiation heat gain
through the single pane window [
BTU / year ] = ( A north season e north
+ A south season e south + A east season
e east + A west season e west ) g
single g blind n cd or hd ##EQU00023##

R.sub.season,d=Radiation heat gain through the double pane window
[BTU/year]edirection=Daily average radiation per unit area on a
vertical wall [BTU/ft2]/day] (Note 2)g single=Solar heat gain
coefficient (SHGC) of single pane window

r total = { r window + r air , i + r air , o ,
when blinds are used r window + r air , i
+ r airgap + r air , o + r blind , when blinds
are not used ##EQU00025##

rwindow=Thermal resistance of the window [ft2°
F.h/BTU]rblind=Thermal resistance of the blind [ft2°
F.h/BTU]rair,i=Thermal resistance of the vertical air film inside
the window [ft2° F.h/BTU]rair,o=Thermal resistance of
the vertical air film outside the window [ft2°
F.h/BTU]rairgap=Thermal resistance of the vertical air film between
the blind and the window [ft2° F.h/BTU]

Baseline Assumptions and Default Values

[0228]1) The sum of difference over a day between Ttarget and outside
temperature is not much different from the difference between
Ttarget and Tavg times 24.

[0249]2) One load means one running cycle of the dryer machine.3)
Inefficiency due to the dirty filter increases proportionally per each
cycle and reaches its maximum of 30% after running 10 cycles. rtime
is the average value over the user's cleaning period. (Note 1)

Default Values for User Inputs:

[0250]r=5[/load]n=2 [load/week]

Monetary Savings

[0251]Net Annual Monetary Savings[$/year]=NEPi,fuel

where Pi,fuel=Price of fuel (gas, oil, or electricity) in the region
where user i lives.

Carbon Savings

[0252]Net Annual Carbon Savings[lb/year]=NEefi

where efi=Emission factor of electricity in the region where user i
lives.

[0255]The Personal Energy Advisor provides a comprehensive,
high-resolution and helpful process for quantifying and reducing global
warming impact throughout an individual or business's life span.

[0256]The system may run all EMS and Personal Energy Advisor calculations
for simultaneous outputs any time any value is modified in either the EMS
or Personal Energy Advisor. The simultaneous outputs include but are not
limited to: carbon dioxide emissions and equivalences in other greenhouse
gases, energy, fuel oil, gasoline, jet fuel, natural gas, electricity,
water, paper, dollars saved, upfront cost, and others. The system filters
and sums the simultaneous outputs of all EMS algorithms into the four
categories and various subcategories. The system performs the same
process for the Personal Energy Advisor algorithms, the outputs of which
are distributed to four categories and the various subcategories
corresponding to those of the EMS. The system subtracts each Personal
Energy Advisor subcategory from the corresponding EMS subcategory to
yield the subcategory outputs. In the event that the Personal Energy
Advisor subcategory value is greater than the EMS subcategory value, the
subcategory is set as null for any of the simultaneous outputs. Each
Personal Energy Advisor subcategory is then aggregated at the category
level to yield four category reduction values for each of the
simultaneous outputs. Each subcategory is aggregated at the category
level to yield four category footprint values for each of the
simultaneous outputs. Each Personal Energy Advisor category is then
aggregated to yield a total reduction value for each of the simultaneous
outputs. The system undergoes the same process for each of the categories
to yield a total footprint value for each of the simultaneous outputs.

[0257]The footprint value can be offset by purchasing additional,
verifiable renewable energy or energy efficiency credits. The quantity of
renewable energy capacity created or energy demand and carbon dioxide
emissions saved is calculated and utilized to determine the user's
"distance" from carbon neutrality. The system is responsible for the
interaction between offset and footprint values, though it appears that
the Personal Energy Advisor is responsible for this interaction on the
Web Site provided in accordance with the present invention. The system
incorporates energy use and carbon dioxide emission offsets to maximize
the ability of consumers and organizations to influence a transition
towards a sustainable future. The user is able to view its initial
footprint, current footprint, reductions, offsets, and quantity away from
carbon neutrality for each of the simultaneous outputs and any
combination of them.

[0258]The system runs various other processes besides the subcategory
interaction linking baseline usage, reductions and offset values in order
to maximize accuracy and customizability for the user. It should be
appreciated that, due to the interaction of the algorithms involved, each
input may change more than one value in more than one subcategory or
category in either the Personal Energy Advisor or the EMS. For example,
if a user commits to install solar panels on their rooftop, this
installation will change the emission factor associated with electricity
use in the user's home. Any actions or purchases that reduce home
electricity use will be updated automatically to take into account this
change in emission factors, thereby maintaining the overall accuracy of
reduction calculations. Because the EMS and the Personal Energy Advisor
interact with one another through a set of feedback mechanisms defined in
the system, the high-resolution character of the Personal Energy Advisor
outputs is not countervailed by even the lowest user engagement levels
with the EMS.

[0259]In addition, the reduction in the user's carbon footprint and energy
use that is determined based on an input value or a change in a
previously input value may be capped based on a subcategory allowance.
For example, if the user indicates that the user has replaced all light
bulbs in the home with energy saving bulbs, the reduction in the carbon
footprint may be capped by the allowance provided for the home appliance
category. This minimizes the influence of human error by preventing a
user from indicating more savings in a specific subcategory than was
previously determined by algorithms comprising that subcategory. Thus,
Personal Energy Advisor subcategory outputs are limited in their ability
to change the aggregate category and total outputs.

[0260]The system also dynamically updates the user's initial footprint
when the user inputs information into EMS or Personal Energy Advisor
algorithms that provide more specific information than those currently
stored in the footprint. For example, if the user initially indicates
that they use a natural gas water heater to heat their water and does not
provide further information, the system assigns that gas water heater an
efficiency rating based on the average natural gas water heater currently
on the market and the average age of water heaters installed in similar
house types in the user's region. The user may later install a low flow
showerhead and indicate at that time the specific age of the water heater
in the home. If the age of the water heater input in the Personal Energy
Advisor algorithm differs from the one used in the EMS algorithm, the
value in the EMS algorithm will be updated, either by being replaced or
being proportionally raised or reduced, depending on the circumstance.
Thus, the more behavioral changes and purchases the user makes, the more
the system learns and adapts to supplement and refine the user's EMS
profile. The system thus gives the EMS and Personal Energy Advisor a lens
on the entire set of data stored for any particular user and thereby
enables each to make the other more precise, customized and
user-friendly.

[0261]The system also accounts for a host of complex interactions between
the EMS and various actions, purchases and behavioral changes. For
example, if a user commits to install a new high-efficient natural gas
fired hot water heater, this will change the emission reductions of any
prior hot water-related actions undertaken by the user. If the user in
question has already installed low flow showerhead, replacing the water
heater reduces the carbon emissions obtained from the low flow showerhead
purchase. By tracking over sixty key variables in the user's profile,
such as water heater age and fuel type, the present invention is able to
account for the entire range of potential interactions between energy
end-use characteristics, behavioral changes, actions and purchases to
adjust the simultaneous outputs. The system thus unites the EMS and the
Personal Energy Advisor to create a comprehensive energy use and carbon
dioxide emissions monitor, customized greening advisor and tracking
system, and personalized e-commerce platform.

[0262]III. Community Connect

[0263]Community Connect is a consumer- and enterprise-facing suite of
software applications designed to engage consumers and businesses around
energy use and their physical communities in a variety of interesting
ways. Community Connect consists of the following interfaces:

[0266]Savings Plan. Intuitive interface that sorts and displays over 300
custom product and action recommendations tailored to customers'
preferences and energy end use profile; customers set savings goals and
receive customized savings plan; feedback given by comparing current and
past bills to savings targets, accounting for temperature and other
changes.

[0268]Neighborhood. Community interface that leverages advanced
geo-location software with billing analytics and the Personal Energy
Advisor to provide usage and savings comparisons for similar homes and
neighbors; customers can become friends with their neighbors, seeing what
actions they are taking to save energy and then recommend actions and
challenge them to reduce energy.

[0269]People. Searchable database of CUB customers that are utilizing the
Community Connect SM software; searches can be done by name, neighborhood
and gender; customers can friend other customers.

[0270]Groups. Searchable database of groups created by CUB customers,
including automatic networks related to neighborhoods.

[0272]Contests page. End users can compete against one another in a host
of contests around reducing energy use and carbon footprints.

[0273]Those skilled in the art will appreciate that the Community Connect
functionality provided in connection with the present invention may be
implemented on the system shown in FIG. 1. For example, the interfaces
described above may be presented as a user interface 12 accessed via a
web site available over the network 16 via the user workstation 10.

[0274]The following list describes a few exemplary features of the
Community Connect portion of the present invention:

[0276]Online community. Robust online community features include activity
feeds, a messaging service, blogs, automated inviter applications,
groups, contests, events, and real-time chat. All of these tools are
adapted to maximize the potential for energy and carbon reductions.

[0277]E-commerce platform. A user's customers can easily compare and
contrast specific energy efficient products and services. Rebates,
coupons and other incentives can also be linked to specific products and
services.

[0278]Geocoding. Geographic location tools that connect users with each
other and energy efficiency products and services. Customers can discover
where they can find the nearest green building or energy auditor while
connecting with their co-worker for a carpool.

[0279]Content integration. Targeted content is a crucial component to
engagement. The software is built to integrate content easily and also
provide custom content from the editorial team.

[0280]Complementary social media tools. Facebook, iPhone, Twitter, and
other relevant social media applications that link actions on the Website
to the rest of the social web.

[0281]Contests platform. A user's customers, their neighborhoods, towns
and companies can create contests around specific actions to reduce
energy use, set contest deadlines and judges, and the software
automatically tracks and ranks the participants in the contest,
announcing a winner at the contest deadline.

[0282]IV. Climate Culture Virtual World Game and Social Network

[0283]The Climate Culture Virtual World (CCVW) is a virtual networked
environment and social network that mirrors the actual global warming
impact of the individual or organization and creates a fully immersive
competitive and collaborative experience among consumers, among
organizations, and between consumers and organizations for the purpose of
minimizing human impact on climate change. By providing a link between
virtual and real worlds, it creates a new process for engaging a consumer
or business to understand and decrease its global warming impact.

[0284]Like the Community Connect functionality discussed above, the CCVW
functionality can be implemented on the system shown in FIG. 1.

[0285]The CCVW is inhabited by a customizable avatar that can resemble its
real-world user. The avatar guides the user step-by-step through the
process of reducing the user's global warming impact. The system enables
the CCVW to customize its recommendation system based on the
characteristics of the specific user. Actions taken in the CCVW, such as
travel, car choice, shopping purchases, or job selection provide guidance
for helping a user to reduce global warming in the real world. A specific
percentage of carbon dioxide reduced from the user's baseline footprint
earns the user a specific number of experience points in the virtual
world. The number of points a user accumulates determines the user's
level and status in the virtual world and provides the user with access
to different features, such as avatar customization options and digital
assets in the virtual world.

[0286]The virtual world environment contains no less than thirty
components each with up to seven 3D representations. Hundreds of
graphical components maximize the ability of the virtual world to
differentiate between the diverse energy end-use characteristics of
users. The components of the virtual world may include but are not
limited to: home, apartment complex, mobile home, office, manufacturing
facility, primary school, secondary school, college or university, strip
mall, farmer's market, indoor shopping mall, community center, contests
arena, amusement park and game center, airport, train station, subway,
virtual store, coal plant, oil well, natural gas plant, wind farm, solar
panel farm, reduction center, forest, lake, beach, triumphal arch, space
needle, bio dome, air tram, catamarans, dolphins, whale, modern schooner,
birds, hand glider, eagle, plane glider, ferry, canoes, hot air balloon,
rainbow, and others. Each of these components reflects the user's carbon
dioxide or other resource footprint or the amount of carbon dioxide
emissions or other resources the user has reduced.

[0287]The virtual world reflects the carbon footprint of the user in
absolute terms, meaning that certain features of the user's footprint may
be relatively beyond the user's control. For instance, if the user lives
in a state that relies significantly upon coal-based sources of
electricity, the user may have great difficulty upgrading the user's
home, office and coal plant based on geographic location alone. However
if the user lives in a geographic area that primarily relies upon clean
sources of electricity production, then the user's home, office and coal
plant will likely be displayed in a more attractive manner.

[0288]This fact, referred to as the "West Virginia Problem," supports the
conclusion that social status should not be determined based on the
absolute footprint values of the user. The CCVW therefore bases social
status on the amount of experience points a user accrues. Experience
points are primarily based on the amount the user has reduced its
footprint as a percentage of its initial footprint, including any
refinements thereto. There are also a number of other ways in which users
can accrue experience points, including but not limited to playing games,
taking part in contests, and making smart choices in terms of lifestyle
behavioral changes, actions and purchases, and contests.

[0289]The number of experience points possessed by the user determines the
user's level in the CCVW. The CCVW has no less than seven levels, each of
which specifies a particular set of features and assets to which the user
has access on the Web Site provided in accordance with the present
invention. For instance, at higher levels, the user may enhance the
user's avatar representation through a variety of fun and customizable
digital assets.

[0290]The CCVW also creates a competition to quantify, reduce and verify
global warming impact. The CCVW may also contain a market-leading social
network whereby each major social network component, such as groups or
events, is integrated with the Personal Energy Advisor. This integration
enables, for instance, group members and leaders or event administrators
and participants to learn from and adapt to a host of interesting data
sets. The CCVW offers a host of features that integrate online community
and energy advisory functions.

[0291]The CCVW may also enable consumers and organizations to engage in
timed contests with quantifiable metrics over a wide range of actions.
Any of the actions, purchases or behavioral changes, or combinations
thereof, contained in the Personal Energy Advisor algorithms can be
converted into a contest using technology embedded in the Personal Energy
Advisor. Consumers, businesses, non-profit institutions, schools and
similarly situated parties are empowered to compete in this contests
environment.

[0292]For example, two major environmental organizations may compete to
install the most compact fluorescent light bulbs in their facilities; the
various dorms at a university can compete to reduce hot water usage in
winter months; two rival law firms can compete to recycle the most
aluminum and paper; two towns can compete to reduce tailpipe emissions by
instituting a carpooling system. These examples are representative only
and not intended to be exhaustive. The contests feature contains a number
of various policing mechanisms, such as timing, attestation, file
uploading, confirmation, invalidation and judging options, which enables
the participants to elect the level of rigor with which their contest is
tracked and judged.

[0293]The CCVW may also enable consumers and organizations to form groups.
Groups may be loosely or closely affiliated individuals or entities,
whether existing in only virtual or both virtual and physical space. The
CCVW provides the same carbon dioxide monitoring and reduction service
described above for individuals to groups of any kind. Groups are also
able to engage in a wide range of tasks regarding connectivity between
members, event planning, scheduling, outreach, and others. Representative
examples of data sets related to groups may include total and average
carbon footprint, most popular actions or purchases, total and average
reductions, total and average dollars saved, group's progress over time,
and others.

[0294]The CCVW may also enable the user to create or join groups,
participate in one-time or recurring events, plan and outreach for
events, share news and media regarding events, and connect with other
members surrounding events. The events feature may be integrated into the
Personal Energy Advisor such that the carbon footprint for the event can
be automatically calculated by the number of event attendees, since the
Personal Energy Advisor knows the location of all attendees, as well as
the location of the event. Attendees may specify their means of
transportation when they join the event or, alternatively, the event
calculator uses default values based on location and distance traveled.
For instance, if an attendee specifies a vehicle as the mode of
transportation, the Personal Energy Advisor uses the make, model and year
in the profile unless the user specifies otherwise.

[0295]The events feature thus serves as an automated carbon event
calculator. At higher levels of sophistication, event participants may
specify detailed information related to participation in the event, the
scope of which expands beyond travel emissions and incorporates a variety
of direct and indirect emissions related to event participation. The
participants and/or administrator of the event thus has the option with a
single click of the mouse to make the event carbon neutral by purchasing
additional, renewable energy or energy efficiency credits.

[0296]The CCVW may rely on the Personal Energy Advisor to support
organization accounts provided in accordance with the present invention.
Organization account features provide a robust suite of services that
assist organizations across a wide swath of sustainability needs,
including but not limited to: market-leading carbon dioxide emission,
energy and other resource usage inventories using the Personal Energy
Advisor; a sustainability advisory tool based on a subset of algorithms
that apply specifically to organizations and which are differentiated by
sector and industry; an employee and/or green team forum to enable
transparent, inclusive and cost/benefit-sensitive decision-making
regarding how most effectively to reduce an organization's global warming
impact (this feature relies on the sustainability advisory tool mentioned
immediately above); consumer fan clubs enabling organizations to share
their sustainability efforts, special offers and other useful information
with users who opt in to the fan club; customized algorithms relating to
specific products capable of determining the extent to which such
products reduce carbon dioxide emissions or other resource usage more
effectively than similar products.

[0297]A real-time multi-user game platform may also be provided in
accordance with the present invention. The CCVW enables users to earn
points by playing games that execute offsets donated by third-party
sponsors. The more games the user plays, wins and the higher the score,
the more offsets and points accrue to the user. A representative example
of a multi-user game is "Scrubble." Scrubble requires the user to combine
at least three of the same molecules to scrub the sulfur dioxide, nitrous
oxide and carbon dioxide emissions from a coal-based electricity
generation facility. The user plays the role of a shooter under the clock
who must scrub the emissions at a faster rate than others. Each time a
user successfully executes a three (or four or five) molecule pairing,
the molecules are transferred to the other players, thus making it more
difficult for them to prevail. The amount of carbon dioxide scrubbed in
the game is equated to a real-world value, which is then offset through
the purchase of renewable energy or efficiency credits.

[0298]Other features that may be contained in the CCVW that create a
collaborative and competitive experience to reduce global warming impact
may include: an activity feed notifying users of friends' actions on the
site, such as points accrued, carbon footprint reduced, events attended,
avatars enhanced, and others; universal search for people, groups,
events, contests, companies, organizations, forums; a robust marketplace
wherein consumers recommend, filter and buy products based on their
unique energy end-use preferences; and various statistical, tracking and
visualization tools, such as an automated carbon dioxide emissions
calculator for driving and other transport distances, among others.

[0299]It should now be appreciated that the present invention provides
advantageous methods, apparatus, and systems for greenhouse gas footprint
monitoring. As noted above, the present invention is applicable to
individuals, families, groups of individuals, companies, buildings,
homes, job sites and other entities.

[0300]Although the invention has been described in connection with various
illustrated embodiments, numerous modifications and adaptations may be
made thereto without departing from the spirit and scope of the invention
as set forth in the claims.